Sections:

• Load Data
• EDA
• Rating Prediction
  • Baseline Mean Predictor
  • Latent Factor: Bias only
  • Complete Latent Factor
  • FastFM with no features
  • FASTFM with features
  • Probabilistic Matrix Factorization
  • Linear Regression
  • Random Forest
  • KNN
  • SVR

# standard
import gzip
import json
from collections import defaultdict
import random
import math
import time 
from datetime import datetime

# data and modeling
import pandas as pd
import numpy as np
import tensorflow as tf

# plotting
import matplotlib.pyplot as plt
import matplotlib as mpl
import plotly.express as px

mpl.rcParams['axes.spines.right'] = False
mpl.rcParams['axes.spines.top'] = False

Load Data

Download review and places data from here and put in data directory under same folder as this notebook.

# load places data as dict

data_places = defaultdict(dict)  # gPlusPlaceId: name, price, hours, closed, gps

f = gzip.open("data/places.clean.json.gz")
for l in f:
    place = eval(l)
    
    if not place['gps']: continue
    lat, lon = place['gps']
    if not (-90 <= lat <= 90 and -180 <= lon <= 180): continue
    if not place['address']: continue
        
    data_places[place['gPlusPlaceId']] = {'name': place['name'],
                                          'address': place['address'],
                                          'price': place['price'],
                                          'hours': place['hours'],
                                          'closed': place['closed'],
                                          'gps': place['gps']
                                         }

# # if you save above as json file "data/places.json" for future runs, read places.json below:
# with open("data/places.json", "r") as f:
#     data_places = json.load(f)

# initial check
len(data_places)
data_places

{'104699454385822125632': {'name': 'Diamond Valley Lake Marina',
  'address': ['2615 Angler Ave', 'Hemet, CA 92545'],
  'price': None,
  'hours': [['Monday', [['6:30 am--4:15 pm']]],
   ['Tuesday', [['6:30 am--4:15 pm']]],
   ['Wednesday', [['6:30 am--4:15 pm']], 1],
   ['Thursday', [['6:30 am--4:15 pm']]],
   ['Friday', [['6:30 am--4:15 pm']]],
   ['Saturday', [['6:30 am--4:15 pm']]],
   ['Sunday', [['6:30 am--4:15 pm']]]],
  'closed': False,
  'gps': [33.703804, -117.003209]},
 '103054478949000078829': {'name': 'Blue Ribbon Cleaners',
  'address': ['Parole', 'Annapolis, MD'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [38.979759, -76.547538]},
 '109810290098030327104': {'name': 'Portofino',
  'address': ['ул. Тутаева, 1', 'Nazran, Ingushetia, Russia', '366720'],
  'price': None,
  'hours': [['Monday', [['9:30 am--9:00 pm']]],
   ['Tuesday', [['9:30 am--9:00 pm']]],
   ['Wednesday', [['9:30 am--9:00 pm']], 1],
   ['Thursday', [['9:30 am--9:00 pm']]],
   ['Friday', [['9:30 am--9:00 pm']]],
   ['Saturday', [['9:30 am--9:00 pm']]],
   ['Sunday', [['9:30 am--9:00 pm']]]],
  'closed': False,
  'gps': [43.22776, 44.762726]},
 '100327153115986850675': {'name': "T C's Referee Sports Bar",
  'address': ['5322 W 26th St', 'Sioux Falls, SD 57106'],
  'price': '$$',
  'hours': [['Monday', [['11:00 am--10:00 pm']]],
   ['Tuesday', [['11:00 am--10:00 pm']], 1],
   ['Wednesday', [['11:00 am--10:00 pm']]],
   ['Thursday', [['11:00 am--10:00 pm']]],
   ['Friday', [['11:00 am--1:00 am']]],
   ['Saturday', [['11:00 am--1:00 am']]],
   ['Sunday', [['11:00 am--10:00 pm']]]],
  'closed': False,
  'gps': [43.529494, -96.792244]},
 '103368487323937936043': {'name': 'Carrefour - Palembang Square',
  'address': ['Jl. Angkatan 45',
   'Kompleks Palembang Square',
   'South Sumatra 30137, Indonesia'],
  'price': None,
  'hours': [['Monday', [['8:00 am--9:00 pm']]],
   ['Tuesday', [['8:00 am--9:00 pm']]],
   ['Wednesday', [['8:00 am--9:00 pm']], 1],
   ['Thursday', [['8:00 am--9:00 pm']]],
   ['Friday', [['8:00 am--9:00 pm']]],
   ['Saturday', [['8:00 am--9:00 pm']]],
   ['Sunday', [['8:00 am--9:00 pm']]]],
  'closed': False,
  'gps': [-2.976256, 104.742662]},
 '103903929544828620241': {'name': 'Pizzeria Montana',
  'address': ['Stuttgarter Straße 44', '75378 Bad Liebenzell', 'Germany'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [48.767817, 8.75612]},
 '103848836623683146185': {'name': 'Single Tree Inn',
  'address': ['2033 W Oklahoma Ave', 'Ulysses, KS 67880'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [37.576427, -101.386182]},
 '114537653478141145113': {'name': 'Sekolah Menengah Kebangsaan Tun Abang Haji Openg',
  'address': ['Jalan Demak', 'Petra Jaya', 'Kuching, Sarawak, Malaysia'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [1.572255, 110.362958]},
 '100062881646354125752': {'name': '天山湯治郷',
  'address': ['208 Yumotochaya',
   'Hakone, Ashigarashimo District, Kanagawa 250-0312',
   'Japan'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [35.224978, 139.088382]},
 '118222137795476771294': {'name': 'Old Chicago',
  'address': ['17960 NW Evergreen Pkwy', 'Beaverton, OR 97006'],
  'price': '$$',
  'hours': [['Monday', [['11:00 am--1:00 am']]],
   ['Tuesday', [['11:00 am--1:00 am']]],
   ['Wednesday', [['11:00 am--1:00 am']]],
   ['Thursday', [['11:00 am--1:00 am']], 1],
   ['Friday', [['11:00 am--1:00 am']]],
   ['Saturday', [['11:00 am--1:00 am']]],
   ['Sunday', [['11:00 am--1:00 am']]]],
  'closed': False,
  'gps': [45.535176, -122.862242]},
 '106432060150136868000': {'name': 'China Cottage',
  'address': ['3718 Wilmington Pike', 'Dayton, OH 45429'],
  'price': '$$',
  'hours': [['Monday', [['11:00 am--10:00 pm']]],
   ['Tuesday', [['11:00 am--10:00 pm']]],
   ['Wednesday', [['11:00 am--10:00 pm']], 1],
   ['Thursday', [['11:00 am--10:00 pm']]],
   ['Friday', [['11:00 am--11:00 pm']]],
   ['Saturday', [['11:00 am--11:00 pm']]],
   ['Sunday', [['11:00 am--10:00 pm']]]],
  'closed': False,
  'gps': [39.692899, -84.136173]},
 '111282238562509982741': {'name': 'La Quinta Inn & Suites Ormond Beach/Daytona Beach',
  'address': ['1571 N US Highway 1', 'Ormond Beach, FL 32174'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [29.337011, -81.128647]},
 '104869934485244376571': {'name': "Dicola's Pizzeria",
  'address': ['626 Can Do Expy # 1 , Hazle, PA 18202'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [40.9908, -76.0117]},
 '107096337073237318840': {'name': 'お食事の店長友・財光寺店',
  'address': ['242-1 Zaikoji', 'Hyuga, Miyazaki 883-0021', 'Japan'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [32.414204, 131.63109]},
 '101043078960050199039': {'name': 'Mastercuts',
  'address': ['666 Brandon Town Ctr',
   'Brandon Town Center',
   'Brandon, FL 33511'],
  'price': None,
  'hours': [['Monday', [['10:00 am--9:00 pm']]],
   ['Tuesday', [['10:00 am--9:00 pm']]],
   ['Wednesday', [['10:00 am--9:00 pm']]],
   ['Thursday', [['10:00 am--9:00 pm']], 1],
   ['Friday', [['10:00 am--9:00 pm']]],
   ['Saturday', [['10:00 am--9:00 pm']]],
   ['Sunday', [['11:00 am--6:00 pm']]]],
  'closed': False,
  'gps': [27.926719, -82.326991]},
 '111230142112807464303': {'name': 'Polo Club Cranbrook',
  'address': ['14531 Ella Blvd', 'Houston, TX 77014'],
  'price': None,
  'hours': [['Monday', [['9:00 am--6:00 pm']]],
   ['Tuesday', [['9:00 am--6:00 pm']]],
   ['Wednesday', [['9:00 am--6:00 pm']], 1],
   ['Thursday', [['9:00 am--6:00 pm']]],
   ['Friday', [['9:00 am--6:00 pm']]],
   ['Saturday', [['10:00 am--5:00 pm']]],
   ['Sunday', [['1:00--5:00 pm']]]],
  'closed': False,
  'gps': [29.979443, -95.439439]},
 '102351867462083324344': {'name': 'だるま料理店',
  'address': ['Japan', '〒250-0012 Kanagawa, Odawara, Honcho, ２丁目1−30'],
  'price': None,
  'hours': [['Monday', [['11:00 am--7:00 pm']]],
   ['Tuesday', [['11:00 am--7:00 pm']]],
   ['Wednesday', [['11:00 am--7:00 pm']], 1],
   ['Thursday', [['11:00 am--7:00 pm']]],
   ['Friday', [['11:00 am--7:00 pm']]],
   ['Saturday', [['11:00 am--7:00 pm']]],
   ['Sunday', [['11:00 am--7:00 pm']]]],
  'closed': False,
  'gps': [35.251385, 139.159618]},
 '115604558784949331949': {'name': 'Orritel Hotel',
  'address': ['Survey No. 274/275/2, Bhosale Farm, Wakad-Hinjewadi Road, Hinjewadi',
   'Dange Chowk Rd, Bhatewara Nagar, Hinjewadi Village, Hinjawadi',
   'Pune, Maharashtra 411057, India'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [18.596771, 73.742188]},
 '100781305512646551142': {'name': '까사로까 여의도점',
  'address': ['25 Yeoeuido-dong', 'Yeongdeungpo-gu, Seoul', 'South Korea'],
  'price': None,
  'hours': None,
  'closed': True,
  'gps': [37.524039, 126.926004]},
 '104147185033261390592': {'name': 'Fly Now',
  'address': ['Tha Muang District', 'Kanchanaburi', 'Thailand'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [13.979946, 99.587241]},
 '107457864393084725818': {'name': 'Seminole Metal Finishing Inc.',
  'address': ['967 Explorer Cove', 'Altamonte Springs, FL 32701'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [28.681282, -81.35249]},
 '105455649021131746775': {'name': 'Byron Center High School',
  'address': ['8500 Burlingame Ave SW', 'Byron Center, MI 49315'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [42.810092, -85.702919]},
 '115937906176039685660': {'name': 'Garras Auto Vidros',
  'address': ['R. Prof Eugênia R Perito, 54',
   'Tubarão - SC',
   '88705-370, Brazil'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [-28.471037, -48.990852]},
 '100184392614713668281': {'name': 'Smokey Mountain Wings',
  'address': ['3607 Outdoor Sportsman Pl', 'Kodak, TN 37764'],
  'price': '$$',
  'hours': None,
  'closed': False,
  'gps': [35.98598, -83.610598]},
 '107059752736783728413': {'name': 'San Antonio Moving Company',
  'address': ['San Antonio, TX'],
  'price': None,
  'hours': [['Monday', [['8:00 am--8:00 pm']]],
   ['Tuesday', [['8:00 am--8:00 pm']]],
   ['Wednesday', [['8:00 am--8:00 pm']]],
   ['Thursday', [['8:00 am--8:00 pm']], 1],
   ['Friday', [['8:00 am--8:00 pm']]],
   ['Saturday', [['8:00 am--8:00 pm']]],
   ['Sunday', [['8:00 am--8:00 pm']]]],
  'closed': False,
  'gps': [29.517006, -98.436528]},
 '105176407214255949518': {'name': "Lara's Place",
  'address': ['Bredabaan 1053/B2', '2930 Brasschaat', 'Belgium'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [51.323673, 4.520526]},
 '101218790935826663894': {'name': 'Muten Kura Sushi Iizuka',
  'address': ['8-3 Benbun', 'Iizuka, Fukuoka 820-0088', 'Japan'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [33.621265, 130.670522]},
 '110421660062903864339': {'name': 'Café Langereis',
  'address': ['Amstel 202', '1017 AH Amsterdam', 'Netherlands'],
  'price': None,
  'hours': [['Monday', [['11:00 am--1:00 am']]],
   ['Tuesday', [['11:00 am--1:00 am']]],
   ['Wednesday', [['11:00 am--1:00 am']], 1],
   ['Thursday', [['11:00 am--1:00 am']]],
   ['Friday', [['11:00 am--3:00 am']]],
   ['Saturday', [['11:00 am--3:00 am']]],
   ['Sunday', [['11:00 am--1:00 am']]]],
  'closed': False,
  'gps': [52.366119, 4.900194]},
 '110904357941128849034': {'name': 'Juliet Photography',
  'address': ['8825 Man Of War Dr', 'Waxhaw, NC 28173'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [34.998894, -80.797123]},
 '106077875776555854791': {'name': 'TAB Maclean & District Bowling Club',
  'address': ['1 McLachlan St', 'Maclean NSW 2463', 'Australia'],
  'price': None,
  'hours': [['Monday', [['9:00 am--10:00 pm']]],
   ['Tuesday', [['9:00 am--10:00 pm']]],
   ['Wednesday', [['9:00 am--11:00 pm']], 1],
   ['Thursday', [['9:00 am--11:00 pm']]],
   ['Friday', [['9:00 am--11:30 pm']]],
   ['Saturday', [['9:00 am--11:00 pm']]],
   ['Sunday', [['9:00 am--9:30 pm']]]],
  'closed': False,
  'gps': [-29.455973, 153.199185]},
 '118117723948195890487': {'name': "Hungry Howie's Pizza & Subs",
  'address': ['7130 Airport Hwy', 'Ste 5', 'Holland, OH 43528'],
  'price': None,
  'hours': [['Monday', [['11:00 am--11:00 pm']]],
   ['Tuesday', [['11:00 am--11:00 pm']]],
   ['Wednesday', [['11:00 am--11:00 pm']]],
   ['Thursday', [['11:00 am--11:00 pm']]],
   ['Friday', [['11:00 am--1:00 am']]],
   ['Saturday', [['11:00 am--1:00 am']], 1],
   ['Sunday', [['12:00--11:00 pm']]]],
  'closed': True,
  'gps': [41.610549, -83.712633]},
 '110300304875024740707': {'name': 'Sabatinos Italian Kitchen',
  'address': ['242 Massachusetts Ave', 'Arlington, MA 02474'],
  'price': '$$$',
  'hours': [['Monday', [['10:00 am--10:15 pm']], 1],
   ['Tuesday', [['10:00 am--10:15 pm']]],
   ['Wednesday', [['10:00 am--10:15 pm']]],
   ['Thursday', [['10:00 am--10:15 pm']]],
   ['Friday', [['10:00 am--10:30 pm']]],
   ['Saturday', [['10:00 am--10:30 pm']]],
   ['Sunday', [['10:00 am--9:45 pm']]]],
  'closed': False,
  'gps': [42.406904, -71.143994]},
 '112862169615307660651': {'name': 'Steppingstone Day School Inc',
  'address': ['7740 Vleigh Pl', 'Flushing, NY 11367'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [40.719993, -73.820745]},
 '106416805735152554811': {'name': 'Vaughn Meat Packing Co Inc',
  'address': ['2100 Country Club Rd', 'Greer, SC 29651'],
  'price': None,
  'hours': [['Monday', [['7:00 am--5:00 pm']]],
   ['Tuesday', [['7:00 am--5:00 pm']]],
   ['Wednesday', [['7:00 am--5:00 pm']], 1],
   ['Thursday', [['7:00 am--5:00 pm']]],
   ['Friday', [['7:00 am--5:00 pm']]],
   ['Saturday', [['7:00 am--2:00 pm']]],
   ['Sunday', [['Closed']]]],
  'closed': False,
  'gps': [34.960876, -82.226628]},
 '114037241892224619421': {'name': '스파리조트 안단테',
  'address': ['Sumun-ri, Anyang-myeon',
   'Jangheung-gun, Jeollanam-do',
   'South Korea'],
  'price': None,
  'hours': [['Monday', [['Open 24 hours']]],
   ['Tuesday', [['Open 24 hours']]],
   ['Wednesday', [['Open 24 hours']]],
   ['Thursday', [['Open 24 hours']], 1],
   ['Friday', [['Open 24 hours']]],
   ['Saturday', [['Open 24 hours']]],
   ['Sunday', [['Open 24 hours']]]],
  'closed': False,
  'gps': [34.638119, 127.042818]},
 '100051048488920343169': {'name': "Thu's Tailor",
  'address': ['529 Jefferson Ave', 'New Orleans, LA 70115'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [29.917074, -90.115715]},
 '100017985891710031671': {'name': 'Chapel of the Cross',
  'address': ['304 E Franklin St', 'Chapel Hill, NC 27514'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [35.915285, -79.0501]},
 '103062340852821108165': {'name': 'LA ROMA',
  'address': ['Calle Zaragoza, 16',
   '11402 Jerez de la Frontera, Cádiz',
   'Cádiz, Spain'],
  'price': None,
  'hours': [['Monday', [['1:00--4:00 pm'], ['8:00 pm--12:00 am']]],
   ['Tuesday', [['1:00--4:00 pm'], ['8:00 pm--12:00 am']]],
   ['Wednesday', [['1:00--4:00 pm'], ['8:00 pm--12:00 am']], 1],
   ['Thursday', [['1:00--4:00 pm'], ['8:00 pm--12:00 am']]],
   ['Friday', [['1:00--4:00 pm'], ['8:00 pm--12:00 am']]],
   ['Saturday', [['1:00--4:00 pm'], ['8:00 pm--12:00 am']]],
   ['Sunday', [['1:00--4:00 pm'], ['8:00 pm--12:00 am']]]],
  'closed': False,
  'gps': [36.688028, -6.13466]},
 '102297455696045037925': {'name': 'HSBC Head Quarters',
  'address': ['8 Canada Sq', 'London E14 5HQ', 'United Kingdom'],
  'price': None,
  'hours': [['Monday', [['8:00 am--7:00 pm']]],
   ['Tuesday', [['9:30 am--7:00 pm']]],
   ['Wednesday', [['8:00 am--7:00 pm']], 1],
   ['Thursday', [['8:00 am--7:00 pm']]],
   ['Friday', [['9:00 am--7:00 pm']]],
   ['Saturday', [['10:00 am--6:00 pm']]],
   ['Sunday', [['12:00--6:00 pm']]]],
  'closed': False,
  'gps': [51.506582, -0.016885]},
 '113292579149737492154': {'name': 'Std Isd',
  'address': ['12-A, M T H Road, M T H Road',
   'Telephone Exchange Rd, Ambattur Industrial Estate',
   'Chennai, Tamil Nadu 600053, India'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [13.101418, 80.162066]},
 '107630647505995708542': {'name': 'Consulate General of the Republic of Poland',
  'address': ['233 Madison Ave', 'New York, NY 10016'],
  'price': None,
  'hours': [['Monday', [['8:00 am--4:00 pm']], 1],
   ['Tuesday', [['8:00 am--4:00 pm']]],
   ['Wednesday', [['11:00 am--7:00 pm']]],
   ['Thursday', [['8:00 am--4:00 pm']]],
   ['Friday', [['8:00 am--4:00 pm']]],
   ['Saturday', [['Closed']]],
   ['Sunday', [['Closed']]]],
  'closed': False,
  'gps': [40.74968, -73.981173]},
 '101900668885771386501': {'name': 'Shubham Super Market',
  'address': ['Shop No 4/5, Viman Nagar, Viman Nagar',
   'Sakore Nagar, Viman Nagar',
   'Pune, Maharashtra 411014, India'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [18.562147, 73.913644]},
 '101886054451353290361': {'name': 'Bale Sandwich & Vietnamese Foods',
  'address': ['1221 Honoapiilani Hwy', 'Lahaina, HI 96761'],
  'price': '$$$',
  'hours': [['Monday', [['9:30 am--9:00 pm']]],
   ['Tuesday', [['9:30 am--9:00 pm']], 1],
   ['Wednesday', [['9:30 am--9:00 pm']]],
   ['Thursday', [['9:30 am--9:00 pm']]],
   ['Friday', [['9:30 am--9:00 pm']]],
   ['Saturday', [['9:30 am--9:00 pm']]],
   ['Sunday', [['9:30 am--7:00 pm']]]],
  'closed': False,
  'gps': [57.295779, -180.0]},
 '112728625344817603614': {'name': 'Scuola Media Statale Antonino De Stefano',
  'address': ['Via Salvatore Caruso, 1',
   '91016 Casa Santa, Erice Trapani',
   'Italy'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [38.022947, 12.547262]},
 '116613597224081757967': {'name': 'Medical Óptica La Paz',
  'address': ['La Paz Kalea, 27', '01004 Vitoria-Gasteiz', 'Araba, Spain'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [42.846554, -2.667956]},
 '112480955169876860251': {'name': 'Dixie Lee Chicken',
  'address': ['145 Trans-Canada Hwy', 'Spanish, ON P0P 2A0', 'Canada'],
  'price': None,
  'hours': [['Monday', [['6:30 am--7:00 pm']]],
   ['Tuesday', [['6:30 am--7:00 pm']]],
   ['Wednesday', [['6:30 am--7:00 pm']]],
   ['Thursday', [['6:30 am--7:00 pm']]],
   ['Friday', [['6:30 am--7:00 pm']]],
   ['Saturday', [['6:30 am--7:00 pm']], 1],
   ['Sunday', [['6:30 am--7:00 pm']]]],
  'closed': False,
  'gps': [46.213896, -82.076332]},
 '105861761768082915675': {'name': 'EMJ Corporation',
  'address': ['2034 Hamilton Pl Blvd', 'Chattanooga, TN 37421'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [35.030859, -85.161441]},
 '109038562492379971333': {'name': 'Smoothie King',
  'address': ['1097 Industrial Pkwy', 'Saraland, AL 36571'],
  'price': '$$',
  'hours': None,
  'closed': False,
  'gps': [30.801426, -88.098032]},
 '116624270028504419560': {'name': 'Agrasen Restaurant',
  'address': ['Gopi Talkies Road, Raigarh, Chhattisgarh, 496001',
   'Nayaganj',
   'Raigarh, Chhattisgarh 496001, India'],
  'price': None,
  'hours': [['Monday', [['10:00 am--11:00 pm']]],
   ['Tuesday', [['10:00 am--11:00 pm']]],
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  'hours': [['Monday', [['8:00 am--8:00 pm']]],
   ['Tuesday', [['8:00 am--8:00 pm']]],
   ['Wednesday', [['8:00 am--8:00 pm']], 1],
   ['Thursday', [['8:00 am--8:00 pm']]],
   ['Friday', [['8:00 am--5:00 pm']]],
   ['Saturday', [['9:00 am--1:00 pm']]],
   ['Sunday', [['Closed']]]],
  'closed': False,
  'gps': [51.779709, -1.26565]},
 '115830259701257874213': {'name': 'Canyon Lake Golf Club',
  'address': ['405 Watts Ln', 'Canyon Lake, TX 78133'],
  'price': None,
  'hours': [['Monday', [['7:00 am--8:00 pm']]],
   ['Tuesday', [['7:00 am--8:00 pm']]],
   ['Wednesday', [['7:00 am--8:00 pm']], 1],
   ['Thursday', [['7:00 am--8:00 pm']]],
   ['Friday', [['7:00 am--8:00 pm']]],
   ['Saturday', [['7:00 am--8:00 pm']]],
   ['Sunday', [['7:00 am--8:00 pm']]]],
  'closed': False,
  'gps': [29.854312, -98.254469]},
 '107612110276106612170': {'name': "Kenny's Castaways",
  'address': ['157 Bleecker St', 'New York, NY 10012'],
  'price': '$$$',
  'hours': [['Monday', [['12:00 pm--4:00 am']]],
   ['Tuesday', [['12:00 pm--4:00 am']]],
   ['Wednesday', [['12:00 pm--4:00 am']], 1],
   ['Thursday', [['12:00 pm--4:00 am']]],
   ['Friday', [['12:00 pm--4:00 am']]],
   ['Saturday', [['12:00 pm--4:00 am']]],
   ['Sunday', [['12:00 pm--4:00 am']]]],
  'closed': True,
  'gps': [40.728618, -73.999826]},
 '105589324145535494703': {'name': 'Margheritta',
  'address': ['Rua Professor Sandoval Arroxelas, 700',
   'AL',
   '57035-230, Brazil'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [-9.658987, -35.703783]},
 '106117386555048642465': {'name': 'Hotel Rali',
  'address': ['Avenida Dom Afonso III 180', 'Portugal'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [41.697149, -8.826076]},
 '115824681833026605010': {'name': 'Dr. Carlos E. Mercado, MD',
  'address': ['1307 International Pkwy #2091', 'Lake Mary, FL 32746'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [28.755446, -81.365886]},
 '111174343350605304812': {'name': 'Pax Christi Catholic Church-Eden',
  'address': ['12100 Pioneer Trail', 'Eden Prairie, MN 55347'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [44.831037, -93.43184]},
 '114398178947887512056': {'name': 'BRR Engenharia Construções',
  'address': ['Rua Alvarenga, 1396', 'São Paulo - SP', '05509-002, Brazil'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [-23.570263, -46.711526]},
 '101546094881779490014': {'name': 'Villa Mitirapa',
  'address': ['BP 70024', "To'ahotu 98719", 'French Polynesia'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [-17.745156, -149.331005]},
 '114365586944772930779': {'name': 'Cyberlink Europe B.V.',
  'address': ['Tyrellsestraat 2', '6291 AL Vaals', 'Netherlands'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [50.772075, 6.019927]},
 '105662612185980890379': {'name': 'El Paseo',
  'address': ['17 Throckmorton Ave', 'Mill Valley, CA 94941'],
  'price': '$',
  'hours': [['Monday', [['5:30--10:00 pm']], 1],
   ['Tuesday', [['5:30--10:00 pm']]],
   ['Wednesday', [['5:30--10:00 pm']]],
   ['Thursday', [['5:30--10:00 pm']]],
   ['Friday', [['5:30--10:00 pm']]],
   ['Saturday', [['5:30--10:00 pm']]],
   ['Sunday', [['5:30--10:00 pm']]]],
  'closed': False,
  'gps': [37.906457, -122.546475]},
 '103838863146554857667': {'name': 'Tasty Bites',
  'address': ['Kolbad Rd, Thane West', 'Thane, Maharashtra 400601', 'India'],
  'price': None,
  'hours': None,
  'closed': False,
  'gps': [19.203345, 72.973753]},
 ...}

# load review data, keep only reviews from USA states

data_reviews = []

f = gzip.open("data/reviews.clean.json.gz")
for i, l in enumerate(f):
    if i >= 2e6:
        break
    
    # if review in California, add to dataset
    review = eval(l)
    place = review['gPlusPlaceId']
    if place in data_places:
        address = data_places[place]['address']
        state = address[-1].split(', ')[-1].split(' ')[0]
        if state == 'CA':
            data_reviews.append(review)

# # if you save above as json file "data/reviews.json" for future runs, read below:
# with open("data/reviews.json", "r") as f:
#     data_reviews = json.load(f)

# initial check
len(data_reviews)

133924

data_reviews[0]

{'rating': 4.0,
 'reviewerName': 'william spindler',
 'reviewText': 'Best War Wanton soup in Red Bluff',
 'categories': ['Asian Restaurant', 'Chinese Restaurant'],
 'gPlusPlaceId': '106591714648856494903',
 'unixReviewTime': 1394669496,
 'reviewTime': 'Mar 12, 2014',
 'gPlusUserId': '100000032416892623125'}

data_places[data_reviews[0]['gPlusPlaceId']]

{'name': 'Peking Chinese Restaurant',
 'address': ['860 Main St', 'Red Bluff, CA 96080'],
 'price': '$$$',
 'hours': [['Monday', [['11:00 am--8:30 pm']]],
  ['Tuesday', [['11:00 am--8:30 pm']]],
  ['Wednesday', [['11:00 am--8:30 pm']], 1],
  ['Thursday', [['11:00 am--8:30 pm']]],
  ['Friday', [['11:00 am--8:30 pm']]],
  ['Saturday', [['12:00--8:30 pm']]],
  ['Sunday', [['12:00--8:30 pm']]]],
 'closed': False,
 'gps': [40.179159, -122.236162]}

EDA

# ratings per user & ratings per place

ratingsPerUser = defaultdict(list)
ratingsPerPlace = defaultdict(list)

for review in data_reviews:
    user = review['gPlusUserId']
    place = review['gPlusPlaceId']
    rating = review['rating']
    ratingsPerUser[user].append((place, rating))
    ratingsPerPlace[place].append((user, rating))

# How many users? Avg number of ratings per user? Quantiles of ratings per user?

num_users = len(ratingsPerUser)
print(f"Num users: {num_users}")

num_ratings_per_user = [len(user_ratings) for user_ratings in ratingsPerUser.values()]
avg_num_ratings_per_user = np.mean(num_ratings_per_user)
print(f"Avg num of ratings per user: {avg_num_ratings_per_user: .2f}")

print("Quantiles: ", np.quantile(num_ratings_per_user, [0, .25, .5, .75, 1]))

plt.boxplot(num_ratings_per_user)
plt.title('Distribution of Number of Ratings per User');

Num users: 57821
Avg num of ratings per user:  2.32
Quantiles:  [  1.   1.   1.   2. 514.]

# Avg rating? Quantiles of ratings?
all_ratings = [review['rating'] for review in data_reviews]
print(f"Avg rating: {np.mean(all_ratings): .2f}")
print("Quantiles: ", np.quantile(all_ratings, [0, .25, .5, .75, 1]))

Avg rating:  4.09
Quantiles:  [0. 4. 5. 5. 5.]

# How many places? Avg number of ratings per place? Quantiles of ratings per place?

num_places = len(ratingsPerPlace)
print(f"Num places: {num_places}")

num_ratings_per_place = [len(place_ratings) for place_ratings in ratingsPerPlace.values()]
avg_num_ratings_per_place = np.mean(num_ratings_per_place)
print(f"Avg num of ratings per place: {avg_num_ratings_per_place: .2f}")

print("Quantiles: ", np.quantile(num_ratings_per_place, [0, .25, .5, .75, 1]))

plt.boxplot(num_ratings_per_place)
plt.title('Distribution of Number of Ratings per Place');

Num places: 70644
Avg num of ratings per place:  1.90
Quantiles:  [ 1.  1.  1.  2. 89.]

# Most popular categories for reviews?

# frequency of categories for reviews
categories = defaultdict(int)
for review in data_reviews:
    cats = review['categories']
    if cats:
        for cat in cats:
            categories[cat] += 1     
            
# stats for categories
sorted_cats = sorted(categories, key=categories.get)
sorted_cats_vals = [categories[cat] for cat in sorted_cats] 

print("Quantile: ", np.quantile(sorted_cats_vals, [0, .25, .5, .75, 1]))
print("Proportion of categories with above 10 ratings: ", np.sum(np.array(sorted_cats_vals) > 10) / len(categories))

# Top Categories
plt.bar(sorted_cats[-20:], sorted_cats_vals[-20:])
plt.title('Top 20 Rating Categories');

Quantile:  [1.0000e+00 2.0000e+00 9.0000e+00 4.3000e+01 1.0248e+04]
Proportion of categories with above 10 ratings:  0.47575057736720555

print(sorted_cats[-20:])

['Sandwich Shop', 'Sushi Restaurant', 'Used Car Dealer', 'Cafe', 'Chinese Restaurant', 'Bar', 'Seafood Restaurant', 'Coffee Shop', 'Japanese Restaurant', 'Hotel', 'Hamburger Restaurant', 'Pizza Restaurant', 'Italian Restaurant', 'Latin American Restaurant', 'Fast Food Restaurant', 'European Restaurant', 'Mexican Restaurant', 'Restaurant', 'Asian Restaurant', 'American Restaurant']

# Where are the reviews?

latitudes = []
longitudes = []
addresses = []

for review in data_reviews:
    place = review['gPlusPlaceId']
    if place in data_places:
        lat, lon = data_places[place]['gps']
        latitudes.append(lat)
        longitudes.append(lon)
        addresses.append(data_places[place]['address'])
        
fig = px.scatter_geo(lat=latitudes, lon=longitudes, hover_name=addresses)
fig.update_layout(title='Location of Places', geo_scope='usa')

# filter for users with 5+ ratings

users_filt = set()  # users with 5+ ratings
for user, ratings in ratingsPerUser.items():
    if len(ratings) >= 5:
        users_filt.add(user)

data_reviews_filt = []  # filtered for users with 5+ ratings
for review in data_reviews:
    if review['gPlusUserId'] in users_filt:
        data_reviews_filt.append(review)
        
ratingsPerUser_filt = defaultdict(list)
ratingsPerPlace_filt = defaultdict(list)

for review in data_reviews_filt:
    user = review['gPlusUserId']
    place = review['gPlusPlaceId']
    rating = review['rating']
    ratingsPerUser_filt[user].append((place, rating))
    ratingsPerPlace_filt[place].append((user, rating))

# After filtering:
# How many users? Avg number of ratings per user? Quantiles of ratings per user?

num_users_filt = len(ratingsPerUser_filt)
print(f"Num users: {num_users_filt}")

num_ratings_per_user_filt = [len(user_ratings) for user_ratings in ratingsPerUser_filt.values()]
avg_num_ratings_per_user_filt = np.mean(num_ratings_per_user_filt)
print(f"Avg num of ratings per user: {avg_num_ratings_per_user_filt: .2f}")

print("Quantiles: ", np.quantile(num_ratings_per_user_filt, [0, .25, .5, .75, 1]))

plt.boxplot(num_ratings_per_user_filt)
plt.title('Distribution of Number of Ratings per User (Filtered)');

Num users: 4460
Avg num of ratings per user:  13.98
Quantiles:  [  5.   6.   8.  13. 514.]

# After filtering:
# How many places? Avg number of ratings per place? Quantiles of ratings per place?

num_places_filt = len(ratingsPerPlace_filt)
print(f"Num places: {num_places_filt}")

num_ratings_per_place_filt = [len(place_ratings) for place_ratings in ratingsPerPlace_filt.values()]
avg_num_ratings_per_place_filt = np.mean(num_ratings_per_place_filt)
print(f"Avg num of ratings per place: {avg_num_ratings_per_place_filt: .2f}")

print("Quantiles: ", np.quantile(num_ratings_per_place_filt, [0, .25, .5, .75, 1]))

plt.boxplot(num_ratings_per_place_filt)
plt.title('Distribution of Number of Ratings per Place (Filtered)');

Num places: 36444
Avg num of ratings per place:  1.71
Quantiles:  [ 1.  1.  1.  2. 53.]

# Where are the reviews?

latitudes = []
longitudes = []
names = []

for review in data_reviews_filt:
    place = review['gPlusPlaceId']
    if place in data_places:
        lat, lon = data_places[place]['gps']
        latitudes.append(lat)
        longitudes.append(lon)
        names.append(data_places[place]['name'])
        
fig = px.scatter_geo(lat=latitudes, lon=longitudes, hover_name=names)
fig.update_layout(title='Location of Places (Filtered)', geo_scope='usa')

# make table for rating, year, month, hour
df_time = pd.DataFrame(columns=['rating', 'year', 'month', 'hour'])
for review in data_reviews_filt:
    if review['unixReviewTime']:
        rating = review['rating']
        year = datetime.utcfromtimestamp(review['unixReviewTime']).year
        month = datetime.utcfromtimestamp(review['unixReviewTime']).month
        hour = datetime.utcfromtimestamp(review['unixReviewTime']).hour

        df_time.loc[len(df_time)]  = [rating, int(year), int(month), int(hour)]
        
df_time.year = df_time.year.apply(lambda yr: int(yr))
df_time.month = df_time.month.apply(lambda m: int(m))
df_time.hour = df_time.hour.apply(lambda hr: int(hr))

df_time.head()

	rating	year	month	hour
0	4.0	2014	3	0
1	5.0	2014	3	19
2	5.0	2014	3	0
3	4.0	2014	3	0
4	5.0	2014	3	0

# average rating by year?
plt.plot(df_time.groupby('year')['rating'].mean()[1:])
plt.scatter(1990, df_time.groupby('year')['rating'].mean().iloc[0])
plt.title('Average Rating by Year');

# average rating by month
plt.plot(df_time.groupby('month')['rating'].mean(), color='green')
plt.title('Average Rating by Month');

# average rating by hour
plt.plot(df_time.groupby('hour')['rating'].mean(), color='orange')
plt.title('Average Rating by Hour');

# overall rating distribution?

ratings = [review['rating'] for review in data_reviews_filt]
avg_rating = np.mean(ratings)
print(f"Avg rating: {avg_rating: .2f}")

print("Quantiles: ", np.quantile(ratings, [0, .25, .5, .75, 1]))

plt.boxplot(ratings)
plt.title('Distribution of Ratings');

Avg rating:  4.04
Quantiles:  [0. 3. 4. 5. 5.]

# rating distribution by price?
ratings_none = []
ratings_1 = []
ratings_2 = []
ratings_3 = []

for review in data_reviews_filt:
    price = data_places[review['gPlusPlaceId']]['price']
    rating = review['rating']
    if price is None:
        ratings_none.append(rating)
    elif price == '$':
        ratings_1.append(rating)
    elif price == '$$':
        ratings_2.append(rating)
    elif price == '$$$':
        ratings_3.append(rating)
        
plt.boxplot([ratings_none, ratings_1, ratings_2, ratings_3, ratings], 
            labels=['None', "$", "\\$$", "$$$", "All"])
plt.title('Distribution of Ratings by Price');

Rating Prediction

def MSE(pred, truth):
    return np.mean([(p - t) ** 2 for p, t in zip(pred, truth)])

len(data_reviews_filt)

62338

data_reviews_filt[0]

{'rating': 4.0,
 'reviewerName': 'william spindler',
 'reviewText': 'Best War Wanton soup in Red Bluff',
 'categories': ['Asian Restaurant', 'Chinese Restaurant'],
 'gPlusPlaceId': '106591714648856494903',
 'unixReviewTime': 1394669496,
 'reviewTime': 'Mar 12, 2014',
 'gPlusUserId': '100000032416892623125'}

# split data
# test: last rating per user
# valid: second to last rating per user
# train: rest of ratings per user

train = []
val = []
test = []

for user, ratings in ratingsPerUser_filt.items():
    for rating in ratings[:-2]:
        train.append([user] + list(rating))
    val.append([user] + list(ratings[-2]))
    test.append([user] + list(ratings[-1]))

# enumerate IDs

userIDs = {}
itemIDs = {}

for review in data_reviews_filt:
    u = review['gPlusUserId']
    i = review['gPlusPlaceId']
    if not u in userIDs: userIDs[u] = len(userIDs)
    if not i in itemIDs: itemIDs[i] = len(itemIDs)
        
nUsers = len(userIDs)
nItems = len(itemIDs)

Baseline: mean prediction

labels_train = [rating for _, _, rating in train]
mean_rating = np.mean(labels_train)
pred_train = [mean_rating] * len(train)
print('train mse: ', MSE(pred_train, labels_train))

labels_val = [rating for _, _, rating in val]
pred_val = [mean_rating] * len(val)
print('val mse: ', MSE(pred_val, labels_val))

labels_test = [rating for _, _, rating in test]
pred_test = [mean_rating] * len(test)
print('test mse: ', MSE(pred_test, labels_test))

train mse:  1.1080448861340293
val mse:  1.2982834085054054
test mse:  1.2788264964653766

Latent Factor Model: Bias Only

class LatentFactorModelBiasOnly(tf.keras.Model):
    def __init__(self, mu, lamb):
        super(LatentFactorModelBiasOnly, self).__init__()
        # Initialize to average
        self.alpha = tf.Variable(mu, dtype=tf.float32)
        # Initialize to small random values
        self.betaU = tf.Variable(tf.random.normal([len(userIDs)],stddev=0.001))
        self.betaI = tf.Variable(tf.random.normal([len(itemIDs)],stddev=0.001))
        self.lamb = lamb

    # Prediction for a single instance (useful for evaluation)
    def predict(self, u, i):
        p = self.alpha + self.betaU[u] + self.betaI[i]
        return p

    # Regularizer
    def reg(self):
        return self.lamb * (tf.reduce_sum(self.betaU**2) +\
                            tf.reduce_sum(self.betaI**2))
    
    # Prediction for a sample of instances
    def predictSample(self, sampleU, sampleI):
        u = tf.convert_to_tensor(sampleU, dtype=tf.int32)
        i = tf.convert_to_tensor(sampleI, dtype=tf.int32)
        beta_u = tf.nn.embedding_lookup(self.betaU, u)
        beta_i = tf.nn.embedding_lookup(self.betaI, i)
        pred = self.alpha + beta_u + beta_i
        return pred
    
    # Loss
    def call(self, sampleU, sampleI, sampleR):
        pred = self.predictSample(sampleU, sampleI)
        r = tf.convert_to_tensor(sampleR, dtype=tf.float32)
        return tf.nn.l2_loss(pred - r) / len(sampleR)

def trainingStepBiasOnly(model, interactions):
    Nsamples = 50000
    with tf.GradientTape() as tape:
        sampleU, sampleI, sampleR = [], [], []
        for _ in range(Nsamples):
            u,i,r = random.choice(interactions)
            sampleU.append(userIDs[u])
            sampleI.append(itemIDs[i])
            sampleR.append(r)

        loss = model(sampleU,sampleI,sampleR)
        loss += model.reg()
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients((grad, var) for
        (grad, var) in zip(gradients, model.trainable_variables)
        if grad is not None)
    return loss.numpy()

minMSE = float('inf')
best_lr = None
best_lamb = None
best_model = None
labels_val = [review[2] for review in val]

for lr in (.001, .01, .1):
    for lamb in (1e-4, 1e-3, 1e-2, 1e-1, 1):       
        # initialize optimizer and model
        optimizer = tf.keras.optimizers.Adam(lr)
        modelBiasOnly = LatentFactorModelBiasOnly(mean_rating, lamb)

        # training
        for i in range(50):
            obj = trainingStepBiasOnly(modelBiasOnly, train)

        # predict on validation set
        pred =\
            [modelBiasOnly.predict(userIDs[u],itemIDs[i]).numpy() for u,i,_ in val]
        pred = np.array(pred)
        pred = np.where(pred < 0, 0, pred)  # min bound = 0
        pred = np.where(pred > 5, 5, pred)  # max bound = 5

        # error
        mse = MSE(pred, labels_val)
        if mse < minMSE:
                minMSE = mse
                best_lr = lr
                best_lamb = lamb
                best_model = modelBiasOnly

        print(f'lr = {lr}, lamb = {lamb}: validation MSE = {mse}')
        
print(f'\nThe best model has: lr = {best_lr}, lambda = {best_lamb}, validation mse = {minMSE}')

lr = 0.001, lamb = 0.0001: validation MSE = 1.2650389963014863
lr = 0.001, lamb = 0.001: validation MSE = 1.2730475318090104
lr = 0.001, lamb = 0.01: validation MSE = 1.2924325979546616
lr = 0.001, lamb = 0.1: validation MSE = 1.2975919364997888
lr = 0.001, lamb = 1: validation MSE = 1.2978640738589795
lr = 0.01, lamb = 0.0001: validation MSE = 1.137547834253309
lr = 0.01, lamb = 0.001: validation MSE = 1.250376417853596
lr = 0.01, lamb = 0.01: validation MSE = 1.2914187428535726
lr = 0.01, lamb = 0.1: validation MSE = 1.2971920037864106
lr = 0.01, lamb = 1: validation MSE = 1.2980280281868537
lr = 0.1, lamb = 0.0001: validation MSE = 1.1168864907512077
lr = 0.1, lamb = 0.001: validation MSE = 1.2493022373298948
lr = 0.1, lamb = 0.01: validation MSE = 1.2899378193582816
lr = 0.1, lamb = 0.1: validation MSE = 1.297489148364969
lr = 0.1, lamb = 1: validation MSE = 1.2989149281461507

The best model has: lr = 0.1, lambda = 0.0001, validation mse = 1.1168864907512077

# with bounding output
# The best model has: lr = 0.1, lambda = 0.0001, validation mse = 1.1168864907512077

optimizer = tf.keras.optimizers.Adam(0.1)
modelBiasOnly = LatentFactorModelBiasOnly(mean_rating, 0.0001)

# training
for i in range(50):
    obj = trainingStepBiasOnly(modelBiasOnly, train)

# predict on test set
pred =\
    [modelBiasOnly.predict(userIDs[u],itemIDs[i]).numpy() for u,i,_ in test]
pred = np.array(pred)
pred = np.where(pred < 0, 0, pred)  # min bound = 0
pred = np.where(pred > 5, 5, pred)  # max bound = 5

# error
print(MSE(pred, labels_test))

2022-11-30 06:38:41.630482: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2022-11-30 06:38:41.630583: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)
2022-11-30 06:38:41.630686: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (dsmlp-jupyter-svillama): /proc/driver/nvidia/version does not exist
2022-11-30 06:38:41.682990: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

1.129109715211232

Latent Factor Model: Complete

class LatentFactorModel(tf.keras.Model):
    def __init__(self, mu, K, lamb):
        super(LatentFactorModel, self).__init__()
        # Initialize to average
        self.alpha = tf.Variable(mu, dtype=tf.float32)
        # Initialize to small random values
        self.betaU = tf.Variable(tf.random.normal([len(userIDs)],stddev=0.001))
        self.betaI = tf.Variable(tf.random.normal([len(itemIDs)],stddev=0.001))
        self.gammaU = tf.Variable(tf.random.normal([len(userIDs),K],stddev=0.001))
        self.gammaI = tf.Variable(tf.random.normal([len(itemIDs),K],stddev=0.001))
        self.lamb = lamb

    # Prediction for a single instance (useful for evaluation)
    def predict(self, u, i):
        p = self.alpha + self.betaU[u] + self.betaI[i] +\
            tf.tensordot(self.gammaU[u], self.gammaI[i], 1)
        return p

    # Regularizer
    def reg(self):
        return self.lamb * (tf.reduce_sum(self.betaU**2) +\
                            tf.reduce_sum(self.betaI**2) +\
                            tf.reduce_sum(self.gammaU**2) +\
                            tf.reduce_sum(self.gammaI**2))
    
    # Prediction for a sample of instances
    def predictSample(self, sampleU, sampleI):
        u = tf.convert_to_tensor(sampleU, dtype=tf.int32)
        i = tf.convert_to_tensor(sampleI, dtype=tf.int32)
        beta_u = tf.nn.embedding_lookup(self.betaU, u)
        beta_i = tf.nn.embedding_lookup(self.betaI, i)
        gamma_u = tf.nn.embedding_lookup(self.gammaU, u)
        gamma_i = tf.nn.embedding_lookup(self.gammaI, i)
        pred = self.alpha + beta_u + beta_i +\
               tf.reduce_sum(tf.multiply(gamma_u, gamma_i), 1)
        return pred
    
    # Loss
    def call(self, sampleU, sampleI, sampleR):
        pred = self.predictSample(sampleU, sampleI)
        r = tf.convert_to_tensor(sampleR, dtype=tf.float32)
        return tf.nn.l2_loss(pred - r) / len(sampleR)

def trainingStep(model, interactions):
    Nsamples = 50000
    with tf.GradientTape() as tape:
        sampleU, sampleI, sampleR = [], [], []
        for _ in range(Nsamples):
            u,i,r = random.choice(interactions)
            sampleU.append(userIDs[u])
            sampleI.append(itemIDs[i])
            sampleR.append(r)

        loss = model(sampleU,sampleI,sampleR)
        loss += model.reg()
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients((grad, var) for
                              (grad, var) in zip(gradients, model.trainable_variables)
                              if grad is not None)
    return loss.numpy()

# initialize the model; EXPERIMENT WITH LR, K, and lambda !!!
optimizer = tf.keras.optimizers.Adam(0.01)
modelLFM = LatentFactorModel(mean_rating, 10, 0.0001)

# run 100 iterations of gradient descent
for i in range(100):
    obj = trainingStep(modelLFM, train)
#     if (i % 10 == 9): print("iteration " + str(i+1) + ", objective = " + str(obj))

# prediction
preds_val = []
for u, i, r in val:
    pred = modelLFM.predict(userIDs[u], itemIDs[i]).numpy()
    if pred < 0:
        pred = 0
    elif pred > 5:
        pred = 5
    preds_val.append(pred)

print(MSE(preds_val, labels_val))

optimizer = tf.keras.optimizers.Adam(0.1)
modelLFM = LatentFactorModel(mean_rating, 10, 0.0001)

# training
for i in range(100):
    obj = trainingStep(modelLFM, train)

# predict on test set
pred =\
    [modelLFM.predict(userIDs[u],itemIDs[i]).numpy() for u,i,_ in test]
pred = np.array(pred)
pred = np.where(pred < 0, 0, pred)  # min bound = 0
pred = np.where(pred > 5, 5, pred)  # max bound = 5

# error
print(MSE(pred, labels_test))

1.1178592860281336

Fast-FM with no features:

• source: https://cseweb.ucsd.edu/~jmcauley/pml/code/chap6.html

import scipy
from fastFM import als

# build factorization machine design matrix

nUsers, nItems = len(userIDs), len(itemIDs)

X = scipy.sparse.lil_matrix((len(data_reviews_filt), nUsers + nItems))
for i, review in enumerate(train + val + test):
    user = userIDs[review[0]]
    item = itemIDs[review[1]]
    X[i, user] = 1 # One-hot encoding of user
    X[i, nUsers + item] = 1 # One-hot encoding of item

# target for each row
y = np.array([review[2] for review in train + val + test])

# split data
X_train, X_val, X_test = X[:len(train)], X[len(train): len(train) + len(val)], X[len(train) + len(val):]
y_train, y_val, y_test = y[:len(train)], y[len(train): len(train) + len(val)], y[len(train) + len(val):]

# initialize factorization machine
fm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=5, l2_reg_w=0.1, l2_reg_V=0.5)

# train the model
fm.fit(X_train, y_train)

# predict on val set
y_pred = fm.predict(X_val)
MSE(y_pred, y_val)

18.419784051615174

# predict with bounds [0,5]
y_pred = fm.predict(X_val)

y_pred = np.where(y_pred < 0, 0, y_pred)  # min bound = 0
y_pred = np.where(y_pred > 5, 5, y_pred)  # max bound = 5

MSE(y_pred, y_val)

2.045847544706892

# predict with bounds [0,5]
y_pred = fm.predict(X_test)

y_pred = np.where(y_pred < 0, 0, y_pred)  # min bound = 0
y_pred = np.where(y_pred > 5, 5, y_pred)  # max bound = 5

MSE(y_pred, y_test)

2.021857995943468

def train_ALS(X_train, y_train, X_val, y_val, num_iters, reg_param, ranks):
    """
    Grid Search Function to select the best model based on MSE of hold-out data
    """
    # initial
    min_error = float('inf')
    best_rank = -1
    best_regw = 0
    best_regV = 0
    best_model = None
    for rank in ranks:
        for regw in reg_param:
            for regV in reg_param:
                # train ALS model
                model = als.FMRegression(
                    n_iter=num_iters,
                    init_stdev=0.1,
                    rank=rank,
                    l2_reg_w=regw,
                    l2_reg_V=regV
                )
                model.fit(X_train, y_train)
                
                # predict on validation set
                predictions = model.predict(X_val)
                predictions = np.where(predictions < 0, 0, predictions)  # min bound = 0
                predictions = np.where(predictions > 5, 5, predictions)  # max bound = 5
                
                # get MSE
                error = MSE(predictions, y_val)
                print(f'latent factors = {rank}, regw = {regw}, regV = {regV}: validation MSE is {error}')
                if error < min_error:
                    min_error = error
                    best_rank = rank
                    best_regw = regw
                    best_regV = regV
                    best_model = model
    print(f'\nThe best model has: latent factors = {best_rank}, regw = {best_regw}, regV = {best_regV}')
    return best_model

# hyper-param config
num_iterations = 100
ranks = [4, 6, 8, 10, 12, 14, 16, 18, 20]
reg_params = [0.001, 0.01, 0.05, 0.1, 0.2, 0.5]

# grid search and select best model
start_time = time.time()
final_model = train_ALS(X_train, y_train, X_val, y_val, num_iterations, reg_params, ranks)

print ('Total Runtime: {:.2f} seconds'.format(time.time() - start_time))

# without bounds:
# The best model has: latent factors = 16, regw = 0.1, regV = 0.01
# validation MSE is 18.041808777232184

# with bounds:
# The best model has: latent factors = 4, regw = 0.001, regV = 0.5
# validation MSE is 1.9614244054984518

# initialize factorization machine
fm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=4, l2_reg_w=0.001, l2_reg_V=0.5)

# train the model
fm.fit(X_train, y_train)

# predict on test set
y_pred = fm.predict(X_test)
y_pred = np.where(y_pred < 0, 0, y_pred)  # min bound = 0
y_pred = np.where(y_pred > 5, 5, y_pred)  # max bound = 5

MSE(y_pred, y_test)

2.0374384339590716

Fast-FM with features:

• categories
• year
• month
• hour
• price

data_pair_reviews = defaultdict(dict)

for review in data_reviews_filt:
    user = review['gPlusUserId']
    place = review['gPlusPlaceId']
    data_pair_reviews[(user, place)] = review

# transform categories to one-hot
categoryIDs = {}

for review in data_reviews_filt:
    # categories
    cats = review['categories']
    if cats:
        for cat in cats:
            if not cat in categoryIDs: categoryIDs[cat] = len(categoryIDs)
nCats = len(categoryIDs)

# transform years to one-hot
years = [review['reviewTime'][-4:] if review['reviewTime'] else -1 for review in data_reviews_filt]
yearIDs = {k:v for k, v in zip(np.unique(years)[1:], range(len(np.unique(years)[1:])))}
nYears = len(yearIDs)

yearIDs

{'1990': 0,
 '2002': 1,
 '2003': 2,
 '2004': 3,
 '2005': 4,
 '2006': 5,
 '2007': 6,
 '2008': 7,
 '2009': 8,
 '2010': 9,
 '2011': 10,
 '2012': 11,
 '2013': 12,
 '2014': 13}

# transform month to one-hot
months = [review['reviewTime'][:3] if review['reviewTime'] else -1 for review in data_reviews_filt]
monthIDs = {k:v for k, v in zip(np.unique(months)[1:], range(len(np.unique(months)[1:])))}
nMonths = len(monthIDs)

monthIDs

{'Apr': 0,
 'Aug': 1,
 'Dec': 2,
 'Feb': 3,
 'Jan': 4,
 'Jul': 5,
 'Jun': 6,
 'Mar': 7,
 'May': 8,
 'Nov': 9,
 'Oct': 10,
 'Sep': 11}

# transform hour to one-hot
hours = [datetime.utcfromtimestamp(review['unixReviewTime']).hour if review['unixReviewTime'] else -1 for review in data_reviews_filt]
hourIDs = {k:v for k, v in zip(np.unique(hours)[1:], range(len(np.unique(hours)[1:])))}
nHours = len(hourIDs)

hourIDs

{0: 0,
 1: 1,
 2: 2,
 3: 3,
 4: 4,
 5: 5,
 6: 6,
 7: 7,
 8: 8,
 9: 9,
 10: 10,
 11: 11,
 12: 12,
 13: 13,
 14: 14,
 15: 15,
 16: 16,
 17: 17,
 18: 18,
 19: 19,
 20: 20,
 21: 21,
 22: 22,
 23: 23}

# transform price to one-hot
prices = [info['price'] if info['price'] else -1 for place, info in data_places.items()]
priceIDs = {k:v for k, v in zip(np.unique(prices)[:-1], range(len(np.unique(prices)[:-1])))}
nPrices = len(priceIDs)

priceIDs

{'$': 0, '$$': 1, '$$$': 2}

# mean rating per user
user_mean_rating = defaultdict(float)
for user, ratings in ratingsPerUser_filt.items():
    user_mean_rating[user] = np.mean([rating[1] for rating in ratings])

# mean rating per place
place_mean_rating = defaultdict(float)
for place, ratings in ratingsPerPlace_filt.items():
    place_mean_rating[place] = np.mean([rating[1] for rating in ratings])

# create matrix of user, item, and features

X = scipy.sparse.lil_matrix((len(data_reviews_filt), nUsers + nItems + nCats + nYears + nMonths + nHours + nPrices + 1 + 1))

for i, review in enumerate(train + val + test):
    u, p, r = review
    
    user = userIDs[u]
    item = itemIDs[p]
    X[i, user] = 1  # One-hot encoding of user
    X[i, nUsers + item] = 1  # One-hot encoding of item
    
    place_info = data_places[place]  # place info
    review_info = data_pair_reviews[(u, p)]  # rating info
    
    cats = review_info['categories']
    if cats:
        for cat in cats:
            c = categoryIDs[cat]
            X[i, nUsers + nItems + c] = 1  # One-hot encoding of category

    date = review_info['reviewTime']
    if date:
        yr = yearIDs[date[-4:]]
        mon = monthIDs[date[:3]]
        X[i, nUsers + nItems + nCats + yr] = 1  # One-hot encoding of year
        X[i, nUsers + nItems + nCats + nYears + mon] = 1  # One-hot encoding of month
    
    if review_info['unixReviewTime']:
        hr = datetime.utcfromtimestamp(review_info['unixReviewTime']).hour
        if hr:
            hr = hourIDs[hr]
            X[i, nUsers + nItems + nCats + nYears + nMonths + hr] = 1  # One-hot encoding of hour

    price = place_info['price']
    if price:
        price = priceIDs[price]
        X[i, nUsers + nItems + nCats + nYears + nMonths + nHours + price] = 1  # One-hot encoding of price
        
    X[i, nUsers + nItems + nCats + nYears + nMonths + nHours + nPrices] = user_mean_rating[u]  # user mean rating
    X[i, nUsers + nItems + nCats + nYears + nMonths + nHours + nPrices + 1] = place_mean_rating[p]  # place mean rating

# target for each row
y = np.array([review[2] for review in train + val + test])

# split data
X_train, X_val, X_test = X[:len(train)], X[len(train): len(train) + len(val)], X[len(train) + len(val):]
y_train, y_val, y_test = y[:len(train)], y[len(train): len(train) + len(val)], y[len(train) + len(val):]

# initialize factorization machine
fm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=10, l2_reg_w=0.001, l2_reg_V=0.5)

# train the model
fm.fit(X_train, y_train)

# predict on val set
y_pred = fm.predict(X_val)
MSE(y_pred, y_val)

20.867305264054103

# predict with bounds [0,5]
y_pred = fm.predict(X_val)

y_pred = np.where(y_pred < 0, 0, y_pred)  # min bound = 0
y_pred = np.where(y_pred > 5, 5, y_pred)  # max bound = 5

MSE(y_pred, y_val)

1.9502286897952235

# predict with bounds [0,5]
y_pred = fm.predict(X_test)

y_pred = np.where(y_pred < 0, 0, y_pred)  # min bound = 0
y_pred = np.where(y_pred > 5, 5, y_pred)  # max bound = 5

MSE(y_pred, y_test)

def train_ALS(X_train, y_train, X_val, y_val, num_iters, reg_param, ranks):
    """
    Grid Search Function to select the best model based on MSE of hold-out data
    """
    # initial
    min_error = float('inf')
    best_rank = -1
    best_regw = 0
    best_regV = 0
    best_model = None
    for rank in ranks:
        for regw in reg_param:
            for regV in reg_param:
                # train ALS model
                model = als.FMRegression(
                    n_iter=num_iters,
                    init_stdev=0.1,
                    rank=rank,
                    l2_reg_w=regw,
                    l2_reg_V=regV
                )
                model.fit(X_train, y_train)
                
                # predict on validation set
                predictions = model.predict(X_val)
                predictions = np.where(predictions < 0, 0, predictions)  # min bound = 0
                predictions = np.where(predictions > 5, 5, predictions)  # max bound = 5
                
                # get MSE
                error = MSE(predictions, y_val)
                print(f'latent factors = {rank}, regw = {regw}, regV = {regV}: validation MSE is {error}')
                if error < min_error:
                    min_error = error
                    best_rank = rank
                    best_regw = regw
                    best_regV = regV
                    best_model = model
    print(f'\nThe best model has: latent factors = {best_rank}, regw = {best_regw}, regV = {best_regV}')
    return best_model

# hyper-param config
num_iterations = 100
ranks = [4, 6, 8, 10, 12, 14, 16, 18, 20]
reg_params = [0.001, 0.01, 0.05, 0.1, 0.2, 0.5]

# grid search and select best model
start_time = time.time()
final_model = train_ALS(X_train, y_train, X_val, y_val, num_iterations, reg_params, ranks)

print ('Total Runtime: {:.2f} seconds'.format(time.time() - start_time))

latent factors = 4, regw = 0.001, regV = 0.001: validation MSE is 2.078461845852992
latent factors = 4, regw = 0.001, regV = 0.01: validation MSE is 2.016320531933159
latent factors = 4, regw = 0.001, regV = 0.05: validation MSE is 1.9812050992160108
latent factors = 4, regw = 0.001, regV = 0.1: validation MSE is 1.970325441659142
latent factors = 4, regw = 0.001, regV = 0.2: validation MSE is 1.9566003771666363
latent factors = 4, regw = 0.001, regV = 0.5: validation MSE is 1.9675174816895489
latent factors = 4, regw = 0.01, regV = 0.001: validation MSE is 2.1427712079559056
latent factors = 4, regw = 0.01, regV = 0.01: validation MSE is 2.052920373745308
latent factors = 4, regw = 0.01, regV = 0.05: validation MSE is 2.015377862422777
latent factors = 4, regw = 0.01, regV = 0.1: validation MSE is 2.003038726363805
latent factors = 4, regw = 0.01, regV = 0.2: validation MSE is 1.984808353377892
latent factors = 4, regw = 0.01, regV = 0.5: validation MSE is 1.9736030734890364
latent factors = 4, regw = 0.05, regV = 0.001: validation MSE is 2.2663853930868574
latent factors = 4, regw = 0.05, regV = 0.01: validation MSE is 2.14276684410912
latent factors = 4, regw = 0.05, regV = 0.05: validation MSE is 2.096490318259328
latent factors = 4, regw = 0.05, regV = 0.1: validation MSE is 2.0664428007013718
latent factors = 4, regw = 0.05, regV = 0.2: validation MSE is 2.022020266152927
latent factors = 4, regw = 0.05, regV = 0.5: validation MSE is 2.007023838545973
latent factors = 4, regw = 0.1, regV = 0.001: validation MSE is 2.3222533238509557
latent factors = 4, regw = 0.1, regV = 0.01: validation MSE is 2.15910436069509
latent factors = 4, regw = 0.1, regV = 0.05: validation MSE is 2.1194062401269247
latent factors = 4, regw = 0.1, regV = 0.1: validation MSE is 2.1031507170537593
latent factors = 4, regw = 0.1, regV = 0.2: validation MSE is 2.075103872557365
latent factors = 4, regw = 0.1, regV = 0.5: validation MSE is 2.0128862919643753
latent factors = 4, regw = 0.2, regV = 0.001: validation MSE is 2.354744131093117
latent factors = 4, regw = 0.2, regV = 0.01: validation MSE is 2.146672747744865
latent factors = 4, regw = 0.2, regV = 0.05: validation MSE is 2.1358107448350734
latent factors = 4, regw = 0.2, regV = 0.1: validation MSE is 2.1220688770482616
latent factors = 4, regw = 0.2, regV = 0.2: validation MSE is 2.1001812325368756
latent factors = 4, regw = 0.2, regV = 0.5: validation MSE is 2.024205324488366
latent factors = 4, regw = 0.5, regV = 0.001: validation MSE is 2.488320013242963
latent factors = 4, regw = 0.5, regV = 0.01: validation MSE is 2.153663574308199
latent factors = 4, regw = 0.5, regV = 0.05: validation MSE is 2.127359589372265
latent factors = 4, regw = 0.5, regV = 0.1: validation MSE is 2.125581555524016
latent factors = 4, regw = 0.5, regV = 0.2: validation MSE is 2.113248981895847
latent factors = 4, regw = 0.5, regV = 0.5: validation MSE is 2.071301064266832
latent factors = 6, regw = 0.001, regV = 0.001: validation MSE is 2.098243258894543
latent factors = 6, regw = 0.001, regV = 0.01: validation MSE is 2.0153040353831537
latent factors = 6, regw = 0.001, regV = 0.05: validation MSE is 1.980009832392145
latent factors = 6, regw = 0.001, regV = 0.1: validation MSE is 1.981779599580253
latent factors = 6, regw = 0.001, regV = 0.2: validation MSE is 1.9660385183773117
latent factors = 6, regw = 0.001, regV = 0.5: validation MSE is 1.9703578170673963
latent factors = 6, regw = 0.01, regV = 0.001: validation MSE is 2.1519267110868654
latent factors = 6, regw = 0.01, regV = 0.01: validation MSE is 2.057585573387185
latent factors = 6, regw = 0.01, regV = 0.05: validation MSE is 2.020971906204788
latent factors = 6, regw = 0.01, regV = 0.1: validation MSE is 2.0091265566544743
latent factors = 6, regw = 0.01, regV = 0.2: validation MSE is 1.9881919316037835
latent factors = 6, regw = 0.01, regV = 0.5: validation MSE is 1.976278737548942
latent factors = 6, regw = 0.05, regV = 0.001: validation MSE is 2.280272493369536
latent factors = 6, regw = 0.05, regV = 0.01: validation MSE is 2.144393394637717
latent factors = 6, regw = 0.05, regV = 0.05: validation MSE is 2.1092895873735613
latent factors = 6, regw = 0.05, regV = 0.1: validation MSE is 2.075896436156416
latent factors = 6, regw = 0.05, regV = 0.2: validation MSE is 2.0200855665453243
latent factors = 6, regw = 0.05, regV = 0.5: validation MSE is 2.0159543533803603
latent factors = 6, regw = 0.1, regV = 0.001: validation MSE is 2.306351967062236
latent factors = 6, regw = 0.1, regV = 0.01: validation MSE is 2.149594428984818
latent factors = 6, regw = 0.1, regV = 0.05: validation MSE is 2.130744511361285
latent factors = 6, regw = 0.1, regV = 0.1: validation MSE is 2.1153268304572395
latent factors = 6, regw = 0.1, regV = 0.2: validation MSE is 2.0679743783240174
latent factors = 6, regw = 0.1, regV = 0.5: validation MSE is 2.0154144055838583
latent factors = 6, regw = 0.2, regV = 0.001: validation MSE is 2.3140325229286263
latent factors = 6, regw = 0.2, regV = 0.01: validation MSE is 2.138998962166685
latent factors = 6, regw = 0.2, regV = 0.05: validation MSE is 2.132591742018371
latent factors = 6, regw = 0.2, regV = 0.1: validation MSE is 2.1295045077564665
latent factors = 6, regw = 0.2, regV = 0.2: validation MSE is 2.085651622441253
latent factors = 6, regw = 0.2, regV = 0.5: validation MSE is 2.035083336078563
latent factors = 6, regw = 0.5, regV = 0.001: validation MSE is 2.399465291717973
latent factors = 6, regw = 0.5, regV = 0.01: validation MSE is 2.161257549688345
latent factors = 6, regw = 0.5, regV = 0.05: validation MSE is 2.1299029484478615
latent factors = 6, regw = 0.5, regV = 0.1: validation MSE is 2.128168677491978
latent factors = 6, regw = 0.5, regV = 0.2: validation MSE is 2.107934515135193
latent factors = 6, regw = 0.5, regV = 0.5: validation MSE is 2.0551206590908513
latent factors = 8, regw = 0.001, regV = 0.001: validation MSE is 2.04084762946134
latent factors = 8, regw = 0.001, regV = 0.01: validation MSE is 1.9868021392321797
latent factors = 8, regw = 0.001, regV = 0.05: validation MSE is 1.9732118978048234
latent factors = 8, regw = 0.001, regV = 0.1: validation MSE is 1.967640171201843
latent factors = 8, regw = 0.001, regV = 0.2: validation MSE is 1.9616309751869503
latent factors = 8, regw = 0.001, regV = 0.5: validation MSE is 1.9633890852506364
latent factors = 8, regw = 0.01, regV = 0.001: validation MSE is 2.082370468954891
latent factors = 8, regw = 0.01, regV = 0.01: validation MSE is 2.031384411815481
latent factors = 8, regw = 0.01, regV = 0.05: validation MSE is 2.0127708402061244
latent factors = 8, regw = 0.01, regV = 0.1: validation MSE is 1.99566547623971
latent factors = 8, regw = 0.01, regV = 0.2: validation MSE is 1.9927771015238318
latent factors = 8, regw = 0.01, regV = 0.5: validation MSE is 1.9793928458664298
latent factors = 8, regw = 0.05, regV = 0.001: validation MSE is 2.211936644491104
latent factors = 8, regw = 0.05, regV = 0.01: validation MSE is 2.110834939669277
latent factors = 8, regw = 0.05, regV = 0.05: validation MSE is 2.1021084882453724
latent factors = 8, regw = 0.05, regV = 0.1: validation MSE is 2.062714315765897
latent factors = 8, regw = 0.05, regV = 0.2: validation MSE is 2.017445299201978
latent factors = 8, regw = 0.05, regV = 0.5: validation MSE is 2.007878954457106
latent factors = 8, regw = 0.1, regV = 0.001: validation MSE is 2.2749030182820253
latent factors = 8, regw = 0.1, regV = 0.01: validation MSE is 2.120774826936874
latent factors = 8, regw = 0.1, regV = 0.05: validation MSE is 2.119078954050758
latent factors = 8, regw = 0.1, regV = 0.1: validation MSE is 2.1012281891512847
latent factors = 8, regw = 0.1, regV = 0.2: validation MSE is 2.0443211614374253
latent factors = 8, regw = 0.1, regV = 0.5: validation MSE is 2.0168902827776845
latent factors = 8, regw = 0.2, regV = 0.001: validation MSE is 2.2990533526971544
latent factors = 8, regw = 0.2, regV = 0.01: validation MSE is 2.124903684724044
latent factors = 8, regw = 0.2, regV = 0.05: validation MSE is 2.1267445863856635
latent factors = 8, regw = 0.2, regV = 0.1: validation MSE is 2.1223069777203145
latent factors = 8, regw = 0.2, regV = 0.2: validation MSE is 2.0673807181684607
latent factors = 8, regw = 0.2, regV = 0.5: validation MSE is 2.027522067758002
latent factors = 8, regw = 0.5, regV = 0.001: validation MSE is 2.3530242239049683
latent factors = 8, regw = 0.5, regV = 0.01: validation MSE is 2.1323122684104434
latent factors = 8, regw = 0.5, regV = 0.05: validation MSE is 2.135302516070142
latent factors = 8, regw = 0.5, regV = 0.1: validation MSE is 2.124176171414884
latent factors = 8, regw = 0.5, regV = 0.2: validation MSE is 2.095409221519446
latent factors = 8, regw = 0.5, regV = 0.5: validation MSE is 2.0529210638917736
latent factors = 10, regw = 0.001, regV = 0.001: validation MSE is 2.0271559716748673
latent factors = 10, regw = 0.001, regV = 0.01: validation MSE is 1.9812345896809698
latent factors = 10, regw = 0.001, regV = 0.05: validation MSE is 1.9757052817125522
latent factors = 10, regw = 0.001, regV = 0.1: validation MSE is 1.9741395154751917
latent factors = 10, regw = 0.001, regV = 0.2: validation MSE is 1.9670609251316709
latent factors = 10, regw = 0.001, regV = 0.5: validation MSE is 1.9555415642829272
latent factors = 10, regw = 0.01, regV = 0.001: validation MSE is 2.0709653985783523
latent factors = 10, regw = 0.01, regV = 0.01: validation MSE is 2.0250432460123755
latent factors = 10, regw = 0.01, regV = 0.05: validation MSE is 2.0131172612081247
latent factors = 10, regw = 0.01, regV = 0.1: validation MSE is 2.0052318973638985
latent factors = 10, regw = 0.01, regV = 0.2: validation MSE is 1.998457035498987
latent factors = 10, regw = 0.01, regV = 0.5: validation MSE is 1.9739291702473656
latent factors = 10, regw = 0.05, regV = 0.001: validation MSE is 2.1920247312087096
latent factors = 10, regw = 0.05, regV = 0.01: validation MSE is 2.1070369179350172
latent factors = 10, regw = 0.05, regV = 0.05: validation MSE is 2.1061027566451607
latent factors = 10, regw = 0.05, regV = 0.1: validation MSE is 2.0654719573608302
latent factors = 10, regw = 0.05, regV = 0.2: validation MSE is 2.0279952597033852
latent factors = 10, regw = 0.05, regV = 0.5: validation MSE is 2.010694267721541
latent factors = 10, regw = 0.1, regV = 0.001: validation MSE is 2.251759928984973
latent factors = 10, regw = 0.1, regV = 0.01: validation MSE is 2.117709417428607
latent factors = 10, regw = 0.1, regV = 0.05: validation MSE is 2.1231057656605126
latent factors = 10, regw = 0.1, regV = 0.1: validation MSE is 2.1003679500994084
latent factors = 10, regw = 0.1, regV = 0.2: validation MSE is 2.0548783866145985
latent factors = 10, regw = 0.1, regV = 0.5: validation MSE is 2.019286255700388
latent factors = 10, regw = 0.2, regV = 0.001: validation MSE is 2.281230218469287
latent factors = 10, regw = 0.2, regV = 0.01: validation MSE is 2.1256526457572464
latent factors = 10, regw = 0.2, regV = 0.05: validation MSE is 2.1284229459765585
latent factors = 10, regw = 0.2, regV = 0.1: validation MSE is 2.1180442987528485
latent factors = 10, regw = 0.2, regV = 0.2: validation MSE is 2.0716438409911655
latent factors = 10, regw = 0.2, regV = 0.5: validation MSE is 2.039422592780701
latent factors = 10, regw = 0.5, regV = 0.001: validation MSE is 2.316734493455051
latent factors = 10, regw = 0.5, regV = 0.01: validation MSE is 2.1293631605194294
latent factors = 10, regw = 0.5, regV = 0.05: validation MSE is 2.127125277885267
latent factors = 10, regw = 0.5, regV = 0.1: validation MSE is 2.117869920767492
latent factors = 10, regw = 0.5, regV = 0.2: validation MSE is 2.078490895266369
latent factors = 10, regw = 0.5, regV = 0.5: validation MSE is 2.052583357294658
latent factors = 12, regw = 0.001, regV = 0.001: validation MSE is 2.0022527637591416
latent factors = 12, regw = 0.001, regV = 0.01: validation MSE is 1.9797144807175115
latent factors = 12, regw = 0.001, regV = 0.05: validation MSE is 1.9774755457533306
latent factors = 12, regw = 0.001, regV = 0.1: validation MSE is 1.9745528329924384
latent factors = 12, regw = 0.001, regV = 0.2: validation MSE is 1.9665825694441856
latent factors = 12, regw = 0.001, regV = 0.5: validation MSE is 1.9571014011665129
latent factors = 12, regw = 0.01, regV = 0.001: validation MSE is 2.0544289657088872
latent factors = 12, regw = 0.01, regV = 0.01: validation MSE is 2.0290419413643392
latent factors = 12, regw = 0.01, regV = 0.05: validation MSE is 2.015631615048926
latent factors = 12, regw = 0.01, regV = 0.1: validation MSE is 2.00594844898726
latent factors = 12, regw = 0.01, regV = 0.2: validation MSE is 1.9986236510507822
latent factors = 12, regw = 0.01, regV = 0.5: validation MSE is 1.9784192094767983
latent factors = 12, regw = 0.05, regV = 0.001: validation MSE is 2.1752754759455533
latent factors = 12, regw = 0.05, regV = 0.01: validation MSE is 2.111463577317075
latent factors = 12, regw = 0.05, regV = 0.05: validation MSE is 2.1070129808037454
latent factors = 12, regw = 0.05, regV = 0.1: validation MSE is 2.0631823091726216
latent factors = 12, regw = 0.05, regV = 0.2: validation MSE is 2.0329395513485666
latent factors = 12, regw = 0.05, regV = 0.5: validation MSE is 2.0160109811951448
latent factors = 12, regw = 0.1, regV = 0.001: validation MSE is 2.2223626325024215
latent factors = 12, regw = 0.1, regV = 0.01: validation MSE is 2.120948593310631
latent factors = 12, regw = 0.1, regV = 0.05: validation MSE is 2.1231914887411305
latent factors = 12, regw = 0.1, regV = 0.1: validation MSE is 2.0993247992619044
latent factors = 12, regw = 0.1, regV = 0.2: validation MSE is 2.055926289740245
latent factors = 12, regw = 0.1, regV = 0.5: validation MSE is 2.0225259335235823
latent factors = 12, regw = 0.2, regV = 0.001: validation MSE is 2.2568559340918357
latent factors = 12, regw = 0.2, regV = 0.01: validation MSE is 2.1233795280539027
latent factors = 12, regw = 0.2, regV = 0.05: validation MSE is 2.128943163366451
latent factors = 12, regw = 0.2, regV = 0.1: validation MSE is 2.1141616010553697
latent factors = 12, regw = 0.2, regV = 0.2: validation MSE is 2.072433558358196
latent factors = 12, regw = 0.2, regV = 0.5: validation MSE is 2.0445770684840476
latent factors = 12, regw = 0.5, regV = 0.001: validation MSE is 2.287676731648142
latent factors = 12, regw = 0.5, regV = 0.01: validation MSE is 2.1241358313965666
latent factors = 12, regw = 0.5, regV = 0.05: validation MSE is 2.1261004384428133
latent factors = 12, regw = 0.5, regV = 0.1: validation MSE is 2.1173027894443686
latent factors = 12, regw = 0.5, regV = 0.2: validation MSE is 2.0814123072377795
latent factors = 12, regw = 0.5, regV = 0.5: validation MSE is 2.067231132085784
latent factors = 14, regw = 0.001, regV = 0.001: validation MSE is 1.9993573283330368
latent factors = 14, regw = 0.001, regV = 0.01: validation MSE is 1.97707784780854
latent factors = 14, regw = 0.001, regV = 0.05: validation MSE is 1.9781001038089865
latent factors = 14, regw = 0.001, regV = 0.1: validation MSE is 1.9752748315103015
latent factors = 14, regw = 0.001, regV = 0.2: validation MSE is 1.966549800999584
latent factors = 14, regw = 0.001, regV = 0.5: validation MSE is 1.960862572525724
latent factors = 14, regw = 0.01, regV = 0.001: validation MSE is 2.04661363350262
latent factors = 14, regw = 0.01, regV = 0.01: validation MSE is 2.025810277395338
latent factors = 14, regw = 0.01, regV = 0.05: validation MSE is 2.014825699229337
latent factors = 14, regw = 0.01, regV = 0.1: validation MSE is 2.00290787226631
latent factors = 14, regw = 0.01, regV = 0.2: validation MSE is 1.9955007685793158
latent factors = 14, regw = 0.01, regV = 0.5: validation MSE is 1.9816897874011545
latent factors = 14, regw = 0.05, regV = 0.001: validation MSE is 2.177938711029135
latent factors = 14, regw = 0.05, regV = 0.01: validation MSE is 2.1132241736111315
latent factors = 14, regw = 0.05, regV = 0.05: validation MSE is 2.106981625901063
latent factors = 14, regw = 0.05, regV = 0.1: validation MSE is 2.059769610516457
latent factors = 14, regw = 0.05, regV = 0.2: validation MSE is 2.027874556016835
latent factors = 14, regw = 0.05, regV = 0.5: validation MSE is 2.008553055675956
latent factors = 14, regw = 0.1, regV = 0.001: validation MSE is 2.223318902378205
latent factors = 14, regw = 0.1, regV = 0.01: validation MSE is 2.1199963957017176
latent factors = 14, regw = 0.1, regV = 0.05: validation MSE is 2.1250785502602594
latent factors = 14, regw = 0.1, regV = 0.1: validation MSE is 2.0969727966819747
latent factors = 14, regw = 0.1, regV = 0.2: validation MSE is 2.046035306580339
latent factors = 14, regw = 0.1, regV = 0.5: validation MSE is 2.0187863933090076
latent factors = 14, regw = 0.2, regV = 0.001: validation MSE is 2.2500316757171843
latent factors = 14, regw = 0.2, regV = 0.01: validation MSE is 2.1239460290795233
latent factors = 14, regw = 0.2, regV = 0.05: validation MSE is 2.1274463776896733
latent factors = 14, regw = 0.2, regV = 0.1: validation MSE is 2.110428028252516
latent factors = 14, regw = 0.2, regV = 0.2: validation MSE is 2.0718585418594007
latent factors = 14, regw = 0.2, regV = 0.5: validation MSE is 2.044002244872965
latent factors = 14, regw = 0.5, regV = 0.001: validation MSE is 2.292267141347447
latent factors = 14, regw = 0.5, regV = 0.01: validation MSE is 2.1261130868686915
latent factors = 14, regw = 0.5, regV = 0.05: validation MSE is 2.1267655416543727
latent factors = 14, regw = 0.5, regV = 0.1: validation MSE is 2.11630618218804
latent factors = 14, regw = 0.5, regV = 0.2: validation MSE is 2.08224361752592
latent factors = 14, regw = 0.5, regV = 0.5: validation MSE is 2.0777700688879497
latent factors = 16, regw = 0.001, regV = 0.001: validation MSE is 1.992897303280366
latent factors = 16, regw = 0.001, regV = 0.01: validation MSE is 1.9782035310765191
latent factors = 16, regw = 0.001, regV = 0.05: validation MSE is 1.9758362006695926
latent factors = 16, regw = 0.001, regV = 0.1: validation MSE is 1.9698000238800133
latent factors = 16, regw = 0.001, regV = 0.2: validation MSE is 1.9614721757812348
latent factors = 16, regw = 0.001, regV = 0.5: validation MSE is 1.95913958292333
latent factors = 16, regw = 0.01, regV = 0.001: validation MSE is 2.051397380984231
latent factors = 16, regw = 0.01, regV = 0.01: validation MSE is 2.0301360429172917
latent factors = 16, regw = 0.01, regV = 0.05: validation MSE is 2.015943311482763
latent factors = 16, regw = 0.01, regV = 0.1: validation MSE is 2.0026500580113895
latent factors = 16, regw = 0.01, regV = 0.2: validation MSE is 1.9906254266436416
latent factors = 16, regw = 0.01, regV = 0.5: validation MSE is 1.9807515770950759
latent factors = 16, regw = 0.05, regV = 0.001: validation MSE is 2.1793121827320183
latent factors = 16, regw = 0.05, regV = 0.01: validation MSE is 2.1126444416807044
latent factors = 16, regw = 0.05, regV = 0.05: validation MSE is 2.1076662827828434
latent factors = 16, regw = 0.05, regV = 0.1: validation MSE is 2.0635667005134013
latent factors = 16, regw = 0.05, regV = 0.2: validation MSE is 2.0252836765616773
latent factors = 16, regw = 0.05, regV = 0.5: validation MSE is 2.00714547777866
latent factors = 16, regw = 0.1, regV = 0.001: validation MSE is 2.226367564999525
latent factors = 16, regw = 0.1, regV = 0.01: validation MSE is 2.1189781058651236
latent factors = 16, regw = 0.1, regV = 0.05: validation MSE is 2.124068945056447
latent factors = 16, regw = 0.1, regV = 0.1: validation MSE is 2.0937964498178117
latent factors = 16, regw = 0.1, regV = 0.2: validation MSE is 2.0445859767991945
latent factors = 16, regw = 0.1, regV = 0.5: validation MSE is 2.0197280473062866
latent factors = 16, regw = 0.2, regV = 0.001: validation MSE is 2.2573127564400584
latent factors = 16, regw = 0.2, regV = 0.01: validation MSE is 2.1196767949303448
latent factors = 16, regw = 0.2, regV = 0.05: validation MSE is 2.1263693049970414
latent factors = 16, regw = 0.2, regV = 0.1: validation MSE is 2.109651305767284
latent factors = 16, regw = 0.2, regV = 0.2: validation MSE is 2.062031654912706
latent factors = 16, regw = 0.2, regV = 0.5: validation MSE is 2.0471985962771484
latent factors = 16, regw = 0.5, regV = 0.001: validation MSE is 2.274227712229465
latent factors = 16, regw = 0.5, regV = 0.01: validation MSE is 2.1229753136569602
latent factors = 16, regw = 0.5, regV = 0.05: validation MSE is 2.1258123080722306
latent factors = 16, regw = 0.5, regV = 0.1: validation MSE is 2.113816046307762
latent factors = 16, regw = 0.5, regV = 0.2: validation MSE is 2.0746287797060834
latent factors = 16, regw = 0.5, regV = 0.5: validation MSE is 2.0742552302839443
latent factors = 18, regw = 0.001, regV = 0.001: validation MSE is 1.9964723706629877
latent factors = 18, regw = 0.001, regV = 0.01: validation MSE is 1.9792065824023202
latent factors = 18, regw = 0.001, regV = 0.05: validation MSE is 1.9749749236225271
latent factors = 18, regw = 0.001, regV = 0.1: validation MSE is 1.9696781550532654
latent factors = 18, regw = 0.001, regV = 0.2: validation MSE is 1.9634716453951824
latent factors = 18, regw = 0.001, regV = 0.5: validation MSE is 1.9606591086381162
latent factors = 18, regw = 0.01, regV = 0.001: validation MSE is 2.050222284562122
latent factors = 18, regw = 0.01, regV = 0.01: validation MSE is 2.0306149854170514
latent factors = 18, regw = 0.01, regV = 0.05: validation MSE is 2.017574992071019
latent factors = 18, regw = 0.01, regV = 0.1: validation MSE is 2.0036175158644163
latent factors = 18, regw = 0.01, regV = 0.2: validation MSE is 1.9928833174948213
latent factors = 18, regw = 0.01, regV = 0.5: validation MSE is 1.9823471994904913
latent factors = 18, regw = 0.05, regV = 0.001: validation MSE is 2.182281567253461
latent factors = 18, regw = 0.05, regV = 0.01: validation MSE is 2.1122790473745736
latent factors = 18, regw = 0.05, regV = 0.05: validation MSE is 2.1084879825253604
latent factors = 18, regw = 0.05, regV = 0.1: validation MSE is 2.0627616300854057
latent factors = 18, regw = 0.05, regV = 0.2: validation MSE is 2.0306922648844763
latent factors = 18, regw = 0.05, regV = 0.5: validation MSE is 2.0087986982408252
latent factors = 18, regw = 0.1, regV = 0.001: validation MSE is 2.2210447022565556
latent factors = 18, regw = 0.1, regV = 0.01: validation MSE is 2.118859270097652
latent factors = 18, regw = 0.1, regV = 0.05: validation MSE is 2.122949691977984
latent factors = 18, regw = 0.1, regV = 0.1: validation MSE is 2.090775674466717
latent factors = 18, regw = 0.1, regV = 0.2: validation MSE is 2.0409683386362385
latent factors = 18, regw = 0.1, regV = 0.5: validation MSE is 2.0281551810470058
latent factors = 18, regw = 0.2, regV = 0.001: validation MSE is 2.2467450496436827
latent factors = 18, regw = 0.2, regV = 0.01: validation MSE is 2.119161545906701
latent factors = 18, regw = 0.2, regV = 0.05: validation MSE is 2.12625172789347
latent factors = 18, regw = 0.2, regV = 0.1: validation MSE is 2.108343959648411
latent factors = 18, regw = 0.2, regV = 0.2: validation MSE is 2.054322746365672
latent factors = 18, regw = 0.2, regV = 0.5: validation MSE is 2.0514601577612677
latent factors = 18, regw = 0.5, regV = 0.001: validation MSE is 2.283882610508667
latent factors = 18, regw = 0.5, regV = 0.01: validation MSE is 2.121673379506908
latent factors = 18, regw = 0.5, regV = 0.05: validation MSE is 2.12578931155549
latent factors = 18, regw = 0.5, regV = 0.1: validation MSE is 2.111105452741602
latent factors = 18, regw = 0.5, regV = 0.2: validation MSE is 2.068157714215412
latent factors = 18, regw = 0.5, regV = 0.5: validation MSE is 2.0794612675085564
latent factors = 20, regw = 0.001, regV = 0.001: validation MSE is 1.999979751980836
latent factors = 20, regw = 0.001, regV = 0.01: validation MSE is 1.9787312556446257
latent factors = 20, regw = 0.001, regV = 0.05: validation MSE is 1.974221945458344
latent factors = 20, regw = 0.001, regV = 0.1: validation MSE is 1.9678778850392702
latent factors = 20, regw = 0.001, regV = 0.2: validation MSE is 1.9640723936583087
latent factors = 20, regw = 0.001, regV = 0.5: validation MSE is 1.9639571735778945
latent factors = 20, regw = 0.01, regV = 0.001: validation MSE is 2.0468172621015306
latent factors = 20, regw = 0.01, regV = 0.01: validation MSE is 2.030150140147313
latent factors = 20, regw = 0.01, regV = 0.05: validation MSE is 2.01668286392479
latent factors = 20, regw = 0.01, regV = 0.1: validation MSE is 2.001480203864296
latent factors = 20, regw = 0.01, regV = 0.2: validation MSE is 1.9902005475188276
latent factors = 20, regw = 0.01, regV = 0.5: validation MSE is 1.9847722767782237
latent factors = 20, regw = 0.05, regV = 0.001: validation MSE is 2.1792263065021458
latent factors = 20, regw = 0.05, regV = 0.01: validation MSE is 2.1114094417357965
latent factors = 20, regw = 0.05, regV = 0.05: validation MSE is 2.1084036339102377
latent factors = 20, regw = 0.05, regV = 0.1: validation MSE is 2.060099805115285
latent factors = 20, regw = 0.05, regV = 0.2: validation MSE is 2.0286968318477827
latent factors = 20, regw = 0.05, regV = 0.5: validation MSE is 2.0160017200549145
latent factors = 20, regw = 0.1, regV = 0.001: validation MSE is 2.2297790786478573
latent factors = 20, regw = 0.1, regV = 0.01: validation MSE is 2.1180717096015393
latent factors = 20, regw = 0.1, regV = 0.05: validation MSE is 2.1221320253682374
latent factors = 20, regw = 0.1, regV = 0.1: validation MSE is 2.0872660605193625
latent factors = 20, regw = 0.1, regV = 0.2: validation MSE is 2.0480718012976733
latent factors = 20, regw = 0.1, regV = 0.5: validation MSE is 2.0330657181856653
latent factors = 20, regw = 0.2, regV = 0.001: validation MSE is 2.2373171427732053
latent factors = 20, regw = 0.2, regV = 0.01: validation MSE is 2.120121043177091
latent factors = 20, regw = 0.2, regV = 0.05: validation MSE is 2.1250608604699517
latent factors = 20, regw = 0.2, regV = 0.1: validation MSE is 2.104647124720169
latent factors = 20, regw = 0.2, regV = 0.2: validation MSE is 2.0557513517468116
latent factors = 20, regw = 0.2, regV = 0.5: validation MSE is 2.0524409423021956
latent factors = 20, regw = 0.5, regV = 0.001: validation MSE is 2.281512300695311
latent factors = 20, regw = 0.5, regV = 0.01: validation MSE is 2.123313117433603
latent factors = 20, regw = 0.5, regV = 0.05: validation MSE is 2.1250701874995532
latent factors = 20, regw = 0.5, regV = 0.1: validation MSE is 2.109495156362337
latent factors = 20, regw = 0.5, regV = 0.2: validation MSE is 2.070340374868594
latent factors = 20, regw = 0.5, regV = 0.5: validation MSE is 2.078400647985354

The best model has: latent factors = 10, regw = 0.001, regV = 0.5
Total Runtime: 1393.55 seconds

# The best model has: latent factors = 10, regw = 0.001, regV = 0.5
# validation MSE is 1.9555415642829272

model = als.FMRegression(
                    n_iter=100,
                    init_stdev=0.1,
                    rank=10,
                    l2_reg_w=0.001,
                    l2_reg_V=0.5
                )
model.fit(X_train, y_train)

# predict on test set
predictions = model.predict(X_test)
predictions = np.where(predictions < 0, 0, predictions)  # min bound = 0
predictions = np.where(predictions > 5, 5, predictions)  # max bound = 5

# get MSE
print(MSE(predictions, y_test))

1.9455938828028667

Probabilistic Matrix Factorization:

• source: https://www.kaggle.com/code/robottums/probabalistic-matrix-factorization-with-suprise/notebook

!pip install surprise

Defaulting to user installation because normal site-packages is not writeable
Collecting surprise
  Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)
Collecting scikit-surprise
  Downloading scikit-surprise-1.1.3.tar.gz (771 kB)
     |████████████████████████████████| 771 kB 13.4 MB/s eta 0:00:01
Requirement already satisfied: joblib>=1.0.0 in /opt/conda/lib/python3.9/site-packages (from scikit-surprise->surprise) (1.0.1)
Requirement already satisfied: numpy>=1.17.3 in /opt/conda/lib/python3.9/site-packages (from scikit-surprise->surprise) (1.21.1)
Requirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.9/site-packages (from scikit-surprise->surprise) (1.7.0)
Building wheels for collected packages: scikit-surprise
  Building wheel for scikit-surprise (setup.py) ... done
  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp39-cp39-linux_x86_64.whl size=1266386 sha256=02dafeaf34995c51ec74c6ff0843bb4f37968c1fb4ff1d093ae9de8290445ffc
  Stored in directory: /home/svillama/.cache/pip/wheels/c6/3a/46/9b17b3512bdf283c6cb84f59929cdd5199d4e754d596d22784
Successfully built scikit-surprise
Installing collected packages: scikit-surprise, surprise
  WARNING: The script surprise is installed in '/home/svillama/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed scikit-surprise-1.1.3 surprise-0.1

import surprise

df = pd.DataFrame(data=train + val + test, columns=['user_id', 'place_id', 'rating'])

reader = surprise.Reader(rating_scale=(0,5)) 
data = surprise.Dataset.load_from_df(df, reader)

class ProbabilisticMatrixFactorization(surprise.AlgoBase):
# Randomly initializes two Matrices, Stochastic Gradient Descent to be able to optimize the best factorization for ratings.
    def __init__(self,learning_rate,num_epochs,num_factors):
       # super(surprise.AlgoBase)
        self.alpha = learning_rate #learning rate for Stochastic Gradient Descent
        self.num_epochs = num_epochs
        self.num_factors = num_factors
    def fit(self,train):
        #randomly initialize user/item factors from a Gaussian
        P = np.random.normal(0,.1,(train.n_users,self.num_factors))
        Q = np.random.normal(0,.1,(train.n_items,self.num_factors))
        #print('fit')

        for epoch in range(self.num_epochs):
            for u,i,r_ui in train.all_ratings():
                residual = r_ui - np.dot(P[u],Q[i])
                temp = P[u,:] # we want to update them at the same time, so we make a temporary variable. 
                P[u,:] +=  self.alpha * residual * Q[i]
                Q[i,:] +=  self.alpha * residual * temp 

                
        self.P = P
        self.Q = Q

        self.trainset = train
    
    
    def estimate(self,u,i):
        #returns estimated rating for user u and item i. Prerequisite: Algorithm must be fit to training set.
        #check to see if u and i are in the train set:
        #print('gahh')

        if self.trainset.knows_user(u) and self.trainset.knows_item(i):
            #print(u,i, '\n','yep:', self.P[u],self.Q[i])
            #return scalar product of P[u] and Q[i]
            nanCheck = np.dot(self.P[u],self.Q[i])
            
            if np.isnan(nanCheck):
                return self.trainset.global_mean
            else:
                return np.dot(self.P[u,:],self.Q[i,:])
        else:# if its not known we'll return the general average. 
           # print('global mean')
            return self.trainset.global_mean
    

Alg1 = ProbabilisticMatrixFactorization(learning_rate=0.05, num_epochs=4, num_factors=10)
data1 = data.build_full_trainset()
Alg1.fit(data1)

print('Label: ', df.rating.iloc[4])
print('Prediction: ', Alg1.estimate(df.user_id.iloc[4], df.place_id.iloc[4]))

/tmp/ipykernel_337/3245469220.py:19: RuntimeWarning: overflow encountered in multiply
  Q[i,:] +=  self.alpha * residual * temp
/tmp/ipykernel_337/3245469220.py:18: RuntimeWarning: overflow encountered in multiply
  P[u,:] +=  self.alpha * residual * Q[i]
/tmp/ipykernel_337/3245469220.py:18: RuntimeWarning: invalid value encountered in add
  P[u,:] +=  self.alpha * residual * Q[i]

Label:  4.0
Prediction:  4.037296672976355

gs = surprise.model_selection.GridSearchCV(ProbabilisticMatrixFactorization, 
                                           param_grid = {'learning_rate': [0.005,0.01],
                                                         'num_epochs':[5,10],
                                                         'num_factors':[10,20]},
                                           measures=['rmse', 'mae'], 
                                           cv=2)
gs.fit(data)

print('rmse: ', gs.best_score['rmse'], '\nmae: ', gs.best_score['mae'])
best_params = gs.best_params['rmse']
print('rmse: ', gs.best_params['rmse'], '\nmae: ', gs.best_params['mae'])

rmse:  2.7089479379627672 
mae:  2.136323670066565
rmse:  {'learning_rate': 0.01, 'num_epochs': 10, 'num_factors': 20} 
mae:  {'learning_rate': 0.01, 'num_epochs': 10, 'num_factors': 10}

bestVersion = ProbabilisticMatrixFactorization(learning_rate=best_params['learning_rate'],
                                               num_epochs=best_params['num_epochs'],
                                               num_factors=best_params['num_factors'])

# we can use k-fold cross validation to evaluate the best model. 
kSplit = surprise.model_selection.KFold(n_splits=10, shuffle=True)
for train, test in kSplit.split(data):
    bestVersion.fit(train)
    prediction = bestVersion.test(test)
    surprise.accuracy.rmse(prediction, verbose=True)

RMSE: 2.5233
RMSE: 2.5447
RMSE: 2.5172
RMSE: 2.6093
RMSE: 2.5708
RMSE: 2.6163
RMSE: 2.5333
RMSE: 2.5734
RMSE: 2.5231
RMSE: 2.5693

Data for Standard Models:

• year (one-hot)
• month (one-hot)
• hour (one-hot)
• price (one-hot)
• user mean rating
• place mean rating

from sklearn.model_selection import GridSearchCV

data_pair_reviews = defaultdict(dict)

for review in data_reviews_filt:
    user = review['gPlusUserId']
    place = review['gPlusPlaceId']
    data_pair_reviews[(user, place)] = review

# transform years to one-hot
years = [review['reviewTime'][-4:] if review['reviewTime'] else -1 for review in data_reviews_filt]
yearIDs = {k:v for k, v in zip(np.unique(years)[1:], range(len(np.unique(years)[1:])))}
nYears = len(yearIDs)

# transform month to one-hot
months = [review['reviewTime'][:3] if review['reviewTime'] else -1 for review in data_reviews_filt]
monthIDs = {k:v for k, v in zip(np.unique(months)[1:], range(len(np.unique(months)[1:])))}
nMonths = len(monthIDs)

# transform hour to one-hot
hours = [datetime.utcfromtimestamp(review['unixReviewTime']).hour if review['unixReviewTime'] else -1 for review in data_reviews_filt]
hourIDs = {k:v for k, v in zip(np.unique(hours)[1:], range(len(np.unique(hours)[1:])))}
nHours = len(hourIDs)

hourIDs

{0: 0,
 1: 1,
 2: 2,
 3: 3,
 4: 4,
 5: 5,
 6: 6,
 7: 7,
 8: 8,
 9: 9,
 10: 10,
 11: 11,
 12: 12,
 13: 13,
 14: 14,
 15: 15,
 16: 16,
 17: 17,
 18: 18,
 19: 19,
 20: 20,
 21: 21,
 22: 22,
 23: 23}

# transform price to one-hot
prices = [info['price'] if info['price'] else -1 for place, info in data_places.items()]
priceIDs = {k:v for k, v in zip(np.unique(prices)[:-1], range(len(np.unique(prices)[:-1])))}
nPrices = len(priceIDs)

priceIDs

{'$': 0, '$$': 1, '$$$': 2}

# mean rating per user
user_mean_rating = defaultdict(float)
for user, ratings in ratingsPerUser_filt.items():
    user_mean_rating[user] = np.mean([rating[1] for rating in ratings])

# mean rating per place
place_mean_rating = defaultdict(float)
for place, ratings in ratingsPerPlace_filt.items():
    place_mean_rating[place] = np.mean([rating[1] for rating in ratings])

# create matrix of user and item features

X = np.zeros(shape=(len(data_reviews_filt), nYears + nMonths + nHours + nPrices + 1 + 1))

for i, review in enumerate(train + val + test):
    u, p, r = review
    review_info = data_pair_reviews[(u, p)]  # rating info
    place_info = data_places[p]  # place info

    date = review_info['reviewTime']
    if date:
        yr = yearIDs[date[-4:]]
        mon = monthIDs[date[:3]]
        X[i, yr] = 1  # One-hot encoding of year
        X[i, nYears + mon] = 1  # One-hot encoding of month
    
    if review_info['unixReviewTime']:
        hr = datetime.utcfromtimestamp(review_info['unixReviewTime']).hour
        if hr:
            hr = hourIDs[hr]
            X[i, nYears + nMonths + hr] = 1  # One-hot encoding of hour

    price = place_info['price']
    if price:
        price = priceIDs[price]
        X[i, nYears + nMonths + nHours + price] = 1  # One-hot encoding of price
        
    X[i, nYears + nMonths + nHours + nPrices] = user_mean_rating[u]  # user mean rating
    X[i, nYears + nMonths + nHours + nPrices + 1] = place_mean_rating[p]  # place mean rating

# target for each row
y = np.array([review[2] for review in train + val + test])

# split data
X_train, X_val, X_test = X[:len(train)], X[len(train): len(train) + len(val)], X[len(train) + len(val):]
y_train, y_val, y_test = y[:len(train)], y[len(train): len(train) + len(val)], y[len(train) + len(val):]

Linear Regression

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

preds = model.predict(X_val)
preds = np.where(preds < 0, 0, preds)  # min bound = 0
preds = np.where(preds > 5, 5, preds)  # max bound = 5
MSE(preds, y_val)

0.23301093742875706

preds = model.predict(X_test)
preds = np.where(preds < 0, 0, preds)  # min bound = 0
preds = np.where(preds > 5, 5, preds)  # max bound = 5
MSE(preds, y_test)

0.22737760793987016

Random Forest

# ensure same train and validation set are used as earlier recommender system models

from sklearn.model_selection import PredefinedSplit

split_index = [-1] * X_train.shape[0] + [0] * X_val.shape[0]
X = np.concatenate((X_train, X_val), axis=0)
y = np.concatenate((y_train, y_val), axis=0)
pds = PredefinedSplit(test_fold = split_index)

from sklearn.ensemble import RandomForestRegressor

model = RandomForestRegressor(n_estimators=100, max_depth=10, bootstrap=True)
model.fit(X_train, y_train)

RandomForestRegressor(max_depth=10)

preds = model.predict(X_val)
preds = np.where(preds < 0, 0, preds)  # min bound = 0
preds = np.where(preds > 5, 5, preds)  # max bound = 5
MSE(preds, y_val)

0.20320665840419358

n_estimators = [100, 200]  # Number of trees in random forest
max_depth = [10, 20, 30, 40]  # Maximum number of levels in tree

parameters = {'n_estimators': n_estimators,
              'max_depth': max_depth}
print(parameters)

{'n_estimators': [100, 200], 'max_depth': [10, 20, 30, 40]}

rf = RandomForestRegressor()
rf_gridsearch = GridSearchCV(estimator = rf, param_grid = parameters, cv=pds, verbose=2, n_jobs=-1)
rf_gridsearch.fit(X, y)

Fitting 1 folds for each of 8 candidates, totalling 8 fits

GridSearchCV(cv=PredefinedSplit(test_fold=array([-1, -1, ...,  0,  0])),
             estimator=RandomForestRegressor(), n_jobs=-1,
             param_grid={'max_depth': [10, 20, 30, 40],
                         'n_estimators': [100, 200]},
             verbose=2)

preds = rf_gridsearch.predict(X_test)
preds = np.where(preds < 0, 0, preds)  # min bound = 0
preds = np.where(preds > 5, 5, preds)  # max bound = 5

MSE(preds, y_test)

0.19961505515029856

KNN

from sklearn.neighbors import KNeighborsRegressor

model = KNeighborsRegressor(n_neighbors=5)
model.fit(X_train, y_train)

preds = model.predict(X_val)
preds = np.where(preds < 0, 0, preds)  # min bound = 0
preds = np.where(preds > 5, 5, preds)  # max bound = 5
MSE(preds, y_val)

0.277982062780269

parameters = {'n_neighbors': [1, 2, 5, 10]}

knn = KNeighborsRegressor()
knn_gridsearch = GridSearchCV(knn, param_grid=parameters, cv=pds, verbose=2)
knn_gridsearch.fit(X, y)

preds = knn_gridsearch.predict(X_test)
preds = np.where(preds < 0, 0, preds)  # min bound = 0
preds = np.where(preds > 5, 5, preds)  # max bound = 5
MSE(preds, y_test)

Fitting 1 folds for each of 4 candidates, totalling 4 fits
[CV] END ......................................n_neighbors=1; total time=  21.2s
[CV] END ......................................n_neighbors=2; total time=  21.8s
[CV] END ......................................n_neighbors=5; total time=  24.5s
[CV] END .....................................n_neighbors=10; total time=  40.6s

0.26225112107623316

SVM

from sklearn.svm import SVR

model = SVR(kernel='linear', C=1)
model.fit(X_train, y_train)

preds = model.predict(X_val)
preds = np.where(preds < 0, 0, preds)  # min bound = 0
preds = np.where(preds > 5, 5, preds)  # max bound = 5
MSE(preds, y_val)

0.24055087186194585

model = SVR(kernel='rbf', C=1)
model.fit(X_train, y_train)

preds = model.predict(X_val)
preds = np.where(preds < 0, 0, preds)  # min bound = 0
preds = np.where(preds > 5, 5, preds)  # max bound = 5
MSE(preds, y_val)

0.22040371584295282

# select best and compute test MSE

model = SVR(kernel='rbf', C=1)
model.fit(X_train, y_train)

preds = model.predict(X_test)
preds = np.where(preds < 0, 0, preds)  # min bound = 0
preds = np.where(preds > 5, 5, preds)  # max bound = 5
MSE(preds, y_test)

0.21600759764189717

# parameters = {'kernel': ['linear', 'poly', 'rbf'],
#               'C': [1, 5, 10]}

# svr = SVR()
# svr_gridsearch = GridSearchCV(svr, param_grid=parameters, cv=pds, verbose=2, n_jobs=-1)
# svr_gridsearch.fit(X, y)

# preds = svr_gridsearch.predict(X_test)
# preds = np.where(preds < 0, 0, preds)  # min bound = 0
# preds = np.where(preds > 5, 5, preds)  # max bound = 5
# MSE(preds, y_test)

Fitting 1 folds for each of 9 candidates, totalling 9 fits

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/tmp/ipykernel_165/2074588010.py in <module>
      1 svr = SVR()
      2 svr_gridsearch = GridSearchCV(svr, param_grid=parameters, cv=pds, verbose=2, n_jobs=-1)
----> 3 svr_gridsearch.fit(X, y)
      4 
      5 preds = svr_gridsearch.predict(X_test)

/opt/conda/lib/python3.9/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
    839                 return results
    840 
--> 841             self._run_search(evaluate_candidates)
    842 
    843             # multimetric is determined here because in the case of a callable

/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_search.py in _run_search(self, evaluate_candidates)
   1294     def _run_search(self, evaluate_candidates):
   1295         """Search all candidates in param_grid"""
-> 1296         evaluate_candidates(ParameterGrid(self.param_grid))
   1297 
   1298 

/opt/conda/lib/python3.9/site-packages/sklearn/model_selection/_search.py in evaluate_candidates(candidate_params, cv, more_results)
    793                               n_splits, n_candidates, n_candidates * n_splits))
    794 
--> 795                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),
    796                                                        X, y,
    797                                                        train=train, test=test,

/opt/conda/lib/python3.9/site-packages/joblib/parallel.py in __call__(self, iterable)
   1052 
   1053             with self._backend.retrieval_context():
-> 1054                 self.retrieve()
   1055             # Make sure that we get a last message telling us we are done
   1056             elapsed_time = time.time() - self._start_time

/opt/conda/lib/python3.9/site-packages/joblib/parallel.py in retrieve(self)
    931             try:
    932                 if getattr(self._backend, 'supports_timeout', False):
--> 933                     self._output.extend(job.get(timeout=self.timeout))
    934                 else:
    935                     self._output.extend(job.get())

/opt/conda/lib/python3.9/site-packages/joblib/_parallel_backends.py in wrap_future_result(future, timeout)
    540         AsyncResults.get from multiprocessing."""
    541         try:
--> 542             return future.result(timeout=timeout)
    543         except CfTimeoutError as e:
    544             raise TimeoutError from e

/opt/conda/lib/python3.9/concurrent/futures/_base.py in result(self, timeout)
    438                     return self.__get_result()
    439 
--> 440                 self._condition.wait(timeout)
    441 
    442                 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:

/opt/conda/lib/python3.9/threading.py in wait(self, timeout)
    310         try:    # restore state no matter what (e.g., KeyboardInterrupt)
    311             if timeout is None:
--> 312                 waiter.acquire()
    313                 gotit = True
    314             else:

KeyboardInterrupt: 

Prediction Results:

Model: test MSE

• Baseline Mean Only: 1.279
• Bias Only Latent Factor: 1.129

• Complete Latent Factor: 1.118

• Probabilistic FM: -

• FastFM: 2.037

• FastFM with features: 1.946

• Linear Regression: 0.227

• Random Forest: 0.200
• KNN: 0.262
• SVR: 0.216

Input Data to Models:

• user/place ID only:
  • Bias Only Latent Factor
  • Complete Latent Factor
  • Probabilistic FM
  • FastFM
• user/place ID + place category, year, month, hour, price, user mean rating, place mean rating:
  • FastFM with features
• year, month, hour, price, user mean rating, place mean rating:
  • Linear Regression
  • Random Forest
  • KNN
  • SVR